diff --git a/search.js b/search.js index acb7f05..cc1bf1e 100644 --- a/search.js +++ b/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.getRegisteredFunction(e);if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._queue.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i+1,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i,0,n)},t.Pipeline.prototype.remove=function(e){var t=this._queue.indexOf(e);-1!==t&&this._queue.splice(t,1)},t.Pipeline.prototype.run=function(e){for(var t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oConstructing training data for attack models\n\n

Membership status is one-hot encoded with the following scheme:

\n\n

[1,0] or 0 : respective record was part of training data\n[0,1] or 1 : respective record was not part of training data

\n"}, "attack_data.set_seed": {"fullname": "attack_data.set_seed", "modulename": "attack_data", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "attack_data.from_target_data": {"fullname": "attack_data.from_target_data", "modulename": "attack_data", "qualname": "from_target_data", "kind": "function", "doc": "

Use the training and test data to construct the attack dataset.\nSplitting is done according to this scheme:

\n\n

Training data Testing data

\n\n

\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 \u2502 \u2502 \u2502 \u2502 \u2502\n\u2502 B \u2502 C \u2502 \u2502 A \u2502 D \u2502\n\u2514\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2518 \u2514\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2518\n \u2502 \u2502 \u2502 \u2502\n \u2502 \u250c\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502\n B \u2502 A \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502\n \u2502 \u2502 \u2502C \u2502D\n \u2502 \u2502 \u2502 \u2502\n \u25bc \u25bc \u25bc \u25bc

\n\n

\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 \u2502 \u2502 \u2502\n\u2502 A+B \u2502 \u2502 C+D \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\nAttack train data Attack test data

\n", "signature": "(\ttargetTrainData: tensorflow.python.data.ops.dataset_ops.DatasetV2,\ttargetTestData: tensorflow.python.data.ops.dataset_ops.DatasetV2,\ttargetModel: keras.engine.sequential.Sequential,\tlabel: int) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "attack_data.load_attack_data": {"fullname": "attack_data.load_attack_data", "modulename": "attack_data", "qualname": "load_attack_data", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict) -> List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]:", "funcdef": "def"}, "attack_data.save": {"fullname": "attack_data.save", "modulename": "attack_data", "qualname": "save", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict,\tdatasets: List[tensorflow.python.data.ops.dataset_ops.DatasetV2]):", "funcdef": "def"}, "attack_data.shuffle": {"fullname": "attack_data.shuffle", "modulename": "attack_data", "qualname": "shuffle", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2,\tbufferSize=10000) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "attack_data.balance_attack_data": {"fullname": "attack_data.balance_attack_data", "modulename": "attack_data", "qualname": "balance_attack_data", "kind": "function", "doc": "

Make sure that input datasets have equal number of in/out datapoints.

\n", "signature": "(\tdatasets: List[tensorflow.python.data.ops.dataset_ops.DatasetV2]) -> List[tensorflow.python.data.ops.dataset_ops.DatasetV2]:", "funcdef": "def"}, "attack_data.split_dataset": {"fullname": "attack_data.split_dataset", "modulename": "attack_data", "qualname": "split_dataset", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2,\tsplit: float):", "funcdef": "def"}, "attack_data.split_attack_data_for_training": {"fullname": "attack_data.split_attack_data_for_training", "modulename": "attack_data", "qualname": "split_attack_data_for_training", "kind": "function", "doc": "

\n", "signature": "(\tdatasets: List[tensorflow.python.data.ops.dataset_ops.DatasetV2],\tconfig: Dict):", "funcdef": "def"}, "attack_data.get_attack_data": {"fullname": "attack_data.get_attack_data", "modulename": "attack_data", "qualname": "get_attack_data", "kind": "function", "doc": "

This function predicts and then labels the provided datasets on their\nrespective shadow model, thus creating the labeled data needed for the\nattack model.

\n", "signature": "(\tconfig: Dict,\tshadowModels: List[keras.engine.sequential.Sequential],\tshadowDatasets: List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]) -> List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]:", "funcdef": "def"}, "attack_data.from_shadow_models": {"fullname": "attack_data.from_shadow_models", "modulename": "attack_data", "qualname": "from_shadow_models", "kind": "function", "doc": "

Predicts the shadow data on the shadow models themselves and labels it with\n\"in\" and \"out\", for the attack model to train on.

\n", "signature": "(\tconfig: Dict,\tshadowModels: List[keras.engine.sequential.Sequential],\tshadowDatasets: List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]) -> List[tensorflow.python.data.ops.dataset_ops.DatasetV2]:", "funcdef": "def"}, "attack_model": {"fullname": "attack_model", "modulename": "attack_model", "kind": "module", "doc": "

\n"}, "attack_model.set_seed": {"fullname": "attack_model.set_seed", "modulename": "attack_model", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "attack_model.KaggleAttackModel": {"fullname": "attack_model.KaggleAttackModel", "modulename": "attack_model", "qualname": "KaggleAttackModel", "kind": "class", "doc": "

Architecture:\n Fully connected NN,\n 1 hiddenlayer, size 64,\n ReLU activation\n Softmax LAyer

\n\n

One model for each class

\n", "bases": "keras.engine.sequential.Sequential"}, "attack_model.KaggleAttackModel.__init__": {"fullname": "attack_model.KaggleAttackModel.__init__", "modulename": "attack_model", "qualname": "KaggleAttackModel.__init__", "kind": "function", "doc": "

Creates a Sequential model instance.

\n\n

Args:\n layers: Optional list of layers to add to the model.\n name: Optional name for the model.

\n", "signature": "(numClasses: int)"}, "attack_model.load_model": {"fullname": "attack_model.load_model", "modulename": "attack_model", "qualname": "load_model", "kind": "function", "doc": "

Load model from disk.

\n\n

The file name will be constructed from the name argument.

\n", "signature": "(name: str, verbose=True) -> keras.engine.sequential.Sequential:", "funcdef": "def"}, "attack_model.save_model": {"fullname": "attack_model.save_model", "modulename": "attack_model", "qualname": "save_model", "kind": "function", "doc": "

Save model to disk.

\n\n

The file name will be constructed from the name argument.

\n", "signature": "(name: str, model: keras.engine.sequential.Sequential) -> None:", "funcdef": "def"}, "attack_model.train_model": {"fullname": "attack_model.train_model", "modulename": "attack_model", "qualname": "train_model", "kind": "function", "doc": "

\n", "signature": "(\tmodel: keras.engine.sequential.Sequential,\tmodelName: str,\ttrainData: tensorflow.python.data.ops.dataset_ops.DatasetV2,\ttestData: tensorflow.python.data.ops.dataset_ops.DatasetV2,\thyperpar: Dict):", "funcdef": "def"}, "attack_model.evaluate_models": {"fullname": "attack_model.evaluate_models", "modulename": "attack_model", "qualname": "evaluate_models", "kind": "function", "doc": "

\n", "signature": "(\tmodels: List[keras.engine.sequential.Sequential],\tdatasets: List[tensorflow.python.data.ops.dataset_ops.DatasetV2]) -> float:", "funcdef": "def"}, "attack_model.evaluate_model": {"fullname": "attack_model.evaluate_model", "modulename": "attack_model", "qualname": "evaluate_model", "kind": "function", "doc": "

\n", "signature": "(\tmodel: keras.engine.sequential.Sequential,\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2):", "funcdef": "def"}, "attack_model.get_model_name": {"fullname": "attack_model.get_model_name", "modulename": "attack_model", "qualname": "get_model_name", "kind": "function", "doc": "

\n", "signature": "(config: Dict, i: int) -> str:", "funcdef": "def"}, "attack_model.get_attack_models": {"fullname": "attack_model.get_attack_models", "modulename": "attack_model", "qualname": "get_attack_models", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict,\tattackDatasets: List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]) -> List[attack_model.KaggleAttackModel]:", "funcdef": "def"}, "attack_pipeline": {"fullname": "attack_pipeline", "modulename": "attack_pipeline", "kind": "module", "doc": "

\n"}, "attack_pipeline.set_seed": {"fullname": "attack_pipeline.set_seed", "modulename": "attack_pipeline", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "attack_pipeline.load_target_data": {"fullname": "attack_pipeline.load_target_data", "modulename": "attack_pipeline", "qualname": "load_target_data", "kind": "function", "doc": "

Returns tuple trainData, restData.

\n\n

RestData is data unused for training and testing previously.

\n", "signature": "(\tconfig: Dict) -> Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]:", "funcdef": "def"}, "attack_pipeline.run_pipeline": {"fullname": "attack_pipeline.run_pipeline", "modulename": "attack_pipeline", "qualname": "run_pipeline", "kind": "function", "doc": "

\n", "signature": "(targetModel, targetTrainData, targetRestData):", "funcdef": "def"}, "attack_pipeline.process_results": {"fullname": "attack_pipeline.process_results", "modulename": "attack_pipeline", "qualname": "process_results", "kind": "function", "doc": "

\n", "signature": "(precision, recall, precisionPerClass, recallPerClass):", "funcdef": "def"}, "configuration": {"fullname": "configuration", "modulename": "configuration", "kind": "module", "doc": "

This module allows the parsing of YAML config files into a python dict.

\n\n

To load a YAML file located in config/ use the method from_name:

\n\n
\n
config = configuration.from_name("example.yml")\n
\n
\n\n

Access the information of the YAML file by accessing members of the returned\nobject. E.g. if the YAML file has an entry \"seed: 1234\":

\n\n
\n
print(config["seed"])\n1234\n
\n
\n\n

You can also load from a relative or absolute file path:

\n\n
\n
config_relative = configuration.from_rel_path("../example.yml")\nconfig_absolute = configuration.from_abs_path("/home/user/config/example.yml")\n
\n
\n\n

Save the current configuration to config/ using the save_to_file method:

\n\n
\n
save_to_file(config) # Uses current time as filename\nsave_to_file(config,"example.yaml") \n
\n
\n"}, "configuration.save_to_file": {"fullname": "configuration.save_to_file", "modulename": "configuration", "qualname": "save_to_file", "kind": "function", "doc": "

If a configName is passed, this will be used to construct the file\nname. Otherwise the current time is used. Either way, config files are\nwritten into the config/ directory.

\n", "signature": "(configuration: Dict, configName: Optional[str] = None):", "funcdef": "def"}, "configuration.from_abs_path": {"fullname": "configuration.from_abs_path", "modulename": "configuration", "qualname": "from_abs_path", "kind": "function", "doc": "

Load a Configuration from absolute file path.

\n", "signature": "(absoluteFilePath: str) -> Dict:", "funcdef": "def"}, "configuration.from_rel_path": {"fullname": "configuration.from_rel_path", "modulename": "configuration", "qualname": "from_rel_path", "kind": "function", "doc": "

Load a Configuration from relative file path.

\n", "signature": "(fileName: str) -> Dict:", "funcdef": "def"}, "configuration.from_name": {"fullname": "configuration.from_name", "modulename": "configuration", "qualname": "from_name", "kind": "function", "doc": "

Load a Configuration from config/.

\n\n

fileName is the name of the file inside config/.

\n", "signature": "(fileName: str) -> Dict:", "funcdef": "def"}, "configuration.from_cli_options": {"fullname": "configuration.from_cli_options", "modulename": "configuration", "qualname": "from_cli_options", "kind": "function", "doc": "

Take options from CLI and load correct config file.

\n", "signature": "(options: Dict) -> Dict:", "funcdef": "def"}, "datasets": {"fullname": "datasets", "modulename": "datasets", "kind": "module", "doc": "

Loading datasets

\n\n

This module preprocesses the datasets (where necessary) and returns them as \ntf.data.Dataset objects.

\n\n

The main interface is the function load_dataset(datasetName:str). It loads a\ntf.data.Dataset from disk and if none is found, creates one and saves it to\ndisk. That means, it can also be used in advance to ensure that the dataset is\nstored to disk and can be quickly loaded during training.

\n\n

Use it like this:

\n\n
\n
kaggle = load_dataset("kaggle")\n
\n
\n\n

Valid datasetName values are: \"cifar10\", \"cifar100\", \"kaggle\", \"kaggle_2\",\n\"kaggle_10\",\"kaggle_20\",\"kaggle_50\",\"kaggle_100\". Using the key \"kaggle_x\" \nreturns the Kaggle dataset, but clustered into x classes. If the dataset has \nto be constructed, a k-means clustering is done in the backend, so it can take \na while.

\n\n

Shuffling

\n\n

To shuffle a Kaggle dataset (e.g. to construct a training set from random \nsamples) call shuffle_kaggle:

\n\n
\n
kaggle = load_dataset("kaggle")\nkaggle_shuffled = shuffle_kaggle(kaggle)\n
\n
\n\n

Pre-loading for later use

\n\n

There is a conveniance method load_all_datasets, which loads all datasets as\ntf.data.Dataset and saves them to disk, so that they can be quickly loaded in\nthe future.

\n\n

Seeding

\n\n

Use set_seed to set a global seed, used by all random functions:

\n\n
\n
set_seed(1234)\n
\n
\n"}, "datasets.set_seed": {"fullname": "datasets.set_seed", "modulename": "datasets", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "datasets.shuffle": {"fullname": "datasets.shuffle", "modulename": "datasets", "qualname": "shuffle", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "datasets.load_attack": {"fullname": "datasets.load_attack", "modulename": "datasets", "qualname": "load_attack", "kind": "function", "doc": "

\n", "signature": "(\tdatasetName: str,\tverbose=True) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "datasets.load_shadow": {"fullname": "datasets.load_shadow", "modulename": "datasets", "qualname": "load_shadow", "kind": "function", "doc": "

\n", "signature": "(\tdatasetName: str,\tverbose=True) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "datasets.load_numpy_array": {"fullname": "datasets.load_numpy_array", "modulename": "datasets", "qualname": "load_numpy_array", "kind": "function", "doc": "

\n", "signature": "(filename: str):", "funcdef": "def"}, "datasets.save_numpy_array": {"fullname": "datasets.save_numpy_array", "modulename": "datasets", "qualname": "save_numpy_array", "kind": "function", "doc": "

\n", "signature": "(filename: str, array):", "funcdef": "def"}, "datasets.save_target": {"fullname": "datasets.save_target", "modulename": "datasets", "qualname": "save_target", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2,\tdatasetName: str):", "funcdef": "def"}, "datasets.load_target": {"fullname": "datasets.load_target", "modulename": "datasets", "qualname": "load_target", "kind": "function", "doc": "

\n", "signature": "(\tdatasetName: str,\tverbose=True) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "datasets.save_attack": {"fullname": "datasets.save_attack", "modulename": "datasets", "qualname": "save_attack", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2,\tdatasetName: str):", "funcdef": "def"}, "datasets.save_shadow": {"fullname": "datasets.save_shadow", "modulename": "datasets", "qualname": "save_shadow", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2,\tdatasetName: str):", "funcdef": "def"}, "datasets.delete_shadow": {"fullname": "datasets.delete_shadow", "modulename": "datasets", "qualname": "delete_shadow", "kind": "function", "doc": "

\n", "signature": "(datasetName: str):", "funcdef": "def"}, "datasets.load_dataset": {"fullname": "datasets.load_dataset", "modulename": "datasets", "qualname": "load_dataset", "kind": "function", "doc": "

Load a dataset.

\n\n

Valid datasetName values are: \"cifar10\", \"cifar100\", \"kaggle\", \"kaggle_2\",\n\"kaggle_10\",\"kaggle_20\",\"kaggle_50\",\"kaggle_100\".

\n", "signature": "(datasetName: str) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "datasets.split_dataset": {"fullname": "datasets.split_dataset", "modulename": "datasets", "qualname": "split_dataset", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2,\tnumSubsets: int) -> List[tensorflow.python.data.ops.dataset_ops.DatasetV2]:", "funcdef": "def"}, "datasets.load_all_datasets": {"fullname": "datasets.load_all_datasets", "modulename": "datasets", "qualname": "load_all_datasets", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "download": {"fullname": "download", "modulename": "download", "kind": "module", "doc": "

This module downloads all necessary datasets.

\n\n

Usage examples:

\n\n
\n
import download\ndownload.download_cifar100()\ndownload.download_cifar10()\ndownload.download_kaggle()\ndownload.download_all_datasets() # Download all 3 at once\n
\n
\n"}, "download.download_kaggle": {"fullname": "download.download_kaggle", "modulename": "download", "qualname": "download_kaggle", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "download.download_cifar10": {"fullname": "download.download_cifar10", "modulename": "download", "qualname": "download_cifar10", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "download.download_cifar100": {"fullname": "download.download_cifar100", "modulename": "download", "qualname": "download_cifar100", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "download.download_all_datasets": {"fullname": "download.download_all_datasets", "modulename": "download", "qualname": "download_all_datasets", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "download.download_dataset": {"fullname": "download.download_dataset", "modulename": "download", "qualname": "download_dataset", "kind": "function", "doc": "

\n", "signature": "(datasetName: str):", "funcdef": "def"}, "main": {"fullname": "main", "modulename": "main", "kind": "module", "doc": "

\n"}, "main.parse_args": {"fullname": "main.parse_args", "modulename": "main", "qualname": "parse_args", "kind": "function", "doc": "

\n", "signature": "() -> Dict:", "funcdef": "def"}, "main.run_script": {"fullname": "main.run_script", "modulename": "main", "qualname": "run_script", "kind": "function", "doc": "

\n", "signature": "(scriptName: str, configFile: str):", "funcdef": "def"}, "main.prepare": {"fullname": "main.prepare", "modulename": "main", "qualname": "prepare", "kind": "function", "doc": "

\n", "signature": "(configFile: str):", "funcdef": "def"}, "main.evaluate": {"fullname": "main.evaluate", "modulename": "main", "qualname": "evaluate", "kind": "function", "doc": "

\n", "signature": "(config: Dict):", "funcdef": "def"}, "shadow_data": {"fullname": "shadow_data", "modulename": "shadow_data", "kind": "module", "doc": "

This module generates shadow data for training the shadow models.\nIt assumes varying knowledge about the original dataset, depending on the\nattacker capabilities.

\n"}, "shadow_data.set_seed": {"fullname": "shadow_data.set_seed", "modulename": "shadow_data", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "shadow_data.generate_shadow_data_sampling": {"fullname": "shadow_data.generate_shadow_data_sampling", "modulename": "shadow_data", "qualname": "generate_shadow_data_sampling", "kind": "function", "doc": "

Generate synthetic data for the shadow models by randomly sampling data\npoints from the original data set.

\n", "signature": "(\toriginal_data: tensorflow.python.data.ops.dataset_ops.DatasetV2) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "shadow_data.split_shadow_data": {"fullname": "shadow_data.split_shadow_data", "modulename": "shadow_data", "qualname": "split_shadow_data", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict,\tshadowData: tensorflow.python.data.ops.dataset_ops.DatasetV2) -> List[tensorflow.python.data.ops.dataset_ops.DatasetV2]:", "funcdef": "def"}, "shadow_data.load_shadow_data": {"fullname": "shadow_data.load_shadow_data", "modulename": "shadow_data", "qualname": "load_shadow_data", "kind": "function", "doc": "

\n", "signature": "(config: Dict):", "funcdef": "def"}, "shadow_data.get_shadow_data_name": {"fullname": "shadow_data.get_shadow_data_name", "modulename": "shadow_data", "qualname": "get_shadow_data_name", "kind": "function", "doc": "

\n", "signature": "(config: Dict):", "funcdef": "def"}, "shadow_data.get_shadow_data": {"fullname": "shadow_data.get_shadow_data", "modulename": "shadow_data", "qualname": "get_shadow_data", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict,\ttargetDataset,\ttargetModel) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "shadow_data.generate_shadow_data_noisy": {"fullname": "shadow_data.generate_shadow_data_noisy", "modulename": "shadow_data", "qualname": "generate_shadow_data_noisy", "kind": "function", "doc": "

Generate synthetic data for the shadow models by using a noisy version of\nthe original data.\nReturns only the noisy data, no the oririnal data.

\n\n

Arguments:\n fraction: percentage of labels that will be flipped per data record to\n make it \"noisy\"

\n", "signature": "(\toriginal_data: tensorflow.python.data.ops.dataset_ops.DatasetV2,\toutputSize: int,\tfraction: float = 0.1) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "shadow_data.generate_shadow_data_statistic": {"fullname": "shadow_data.generate_shadow_data_statistic", "modulename": "shadow_data", "qualname": "generate_shadow_data_statistic", "kind": "function", "doc": "

Generate synthetic data for the shadow models by using the marginal\ndistribution of features in the original dataset.

\n", "signature": "(\toriginal_data: tensorflow.python.data.ops.dataset_ops.DatasetV2) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "shadow_data.hill_climbing": {"fullname": "shadow_data.hill_climbing", "modulename": "shadow_data", "qualname": "hill_climbing", "kind": "function", "doc": "

Generate synthetic data for the shadow models by querying the target model\nfor randomly sampled records, in order to find those that are classified\nwith high confidence.

\n\n

numRecords: size of generated dataset\nhyperpars has the following keys (taken from the paper:\nk_max,k_min,rej_max,conf_min,iter_max)

\n", "signature": "(\ttargetModel: keras.engine.sequential.Sequential,\tnumRecords: int,\t**hyperpars) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "shadow_data.get_target_model_rest_data": {"fullname": "shadow_data.get_target_model_rest_data", "modulename": "shadow_data", "qualname": "get_target_model_rest_data", "kind": "function", "doc": "

\n", "signature": "(config: Dict) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "shadow_models": {"fullname": "shadow_models", "modulename": "shadow_models", "kind": "module", "doc": "

\n"}, "shadow_models.set_seed": {"fullname": "shadow_models.set_seed", "modulename": "shadow_models", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "shadow_models.get_shadow_model_name": {"fullname": "shadow_models.get_shadow_model_name", "modulename": "shadow_models", "qualname": "get_shadow_model_name", "kind": "function", "doc": "

\n", "signature": "(config: Dict, i: int):", "funcdef": "def"}, "shadow_models.load_shadow_models_and_datasets": {"fullname": "shadow_models.load_shadow_models_and_datasets", "modulename": "shadow_models", "qualname": "load_shadow_models_and_datasets", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict) -> Tuple[List[keras.engine.sequential.Sequential], List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]]:", "funcdef": "def"}, "shadow_models.train_shadow_models": {"fullname": "shadow_models.train_shadow_models", "modulename": "shadow_models", "qualname": "train_shadow_models", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict,\tshadowDatasets: List[tensorflow.python.data.ops.dataset_ops.DatasetV2]) -> Tuple[List[keras.engine.sequential.Sequential], List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]]:", "funcdef": "def"}, "shadow_models.get_shadow_models_and_datasets": {"fullname": "shadow_models.get_shadow_models_and_datasets", "modulename": "shadow_models", "qualname": "get_shadow_models_and_datasets", "kind": "function", "doc": "

Tries to load shadow datasets from disk, alternatively trains from scratch.

\n\n

Returns 2 lists:\n models: the trained shadow models and a list of tuples, containing\n datasets: the training and test data for the corresponding shadow models

\n\n
E.g. models[0] is trained with datasets[0,0] and tested on datasets[0,1]\n
\n", "signature": "(\tconfig: Dict,\tshadowDatasets: List[tensorflow.python.data.ops.dataset_ops.DatasetV2]) -> Tuple[List[keras.engine.sequential.Sequential], List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]]:", "funcdef": "def"}, "target_models": {"fullname": "target_models", "modulename": "target_models", "kind": "module", "doc": "

This module defines the victim models and provides methods to save, load and\ntrain them.

\n\n

To set the seed:

\n\n
\n
victim_models.set_seed(1234)\n
\n
\n\n

To load the models:

\n\n
\n
cifar10Model = victim_models.CifarModel()\nkaggleModel = victim_models.KaggleModel()\n
\n
\n\n

To train it with a tf.data.Dataset called cifar10 (dimension has to fit):

\n\n
\n
victim_models.train_model(cifar10Model, cifar10, epochs=1)\n
\n
\n\n

To save and load the model:

\n\n
\n
victim_models.save_model("filename",cifar10Model)\ncifar10Model = victim_models.load_model("filename")\n
\n
\n"}, "target_models.set_seed": {"fullname": "target_models.set_seed", "modulename": "target_models", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "target_models.CifarModel": {"fullname": "target_models.CifarModel", "modulename": "target_models", "qualname": "CifarModel", "kind": "class", "doc": "

On CIFAR datasets, we train a standard convolutional neural\nnetwork (CNN) with two convolution and max pooling layers\nplus a fully connected layer of size 128 and a Sof tMax layer.\n\u2018We use Tanh as the activation function. We set the learning\nrate to 0.001, the learning rate decay to le \u2014 07, and the\nmaximum epochs of training to 100.

\n", "bases": "keras.engine.sequential.Sequential"}, "target_models.CifarModel.__init__": {"fullname": "target_models.CifarModel.__init__", "modulename": "target_models", "qualname": "CifarModel.__init__", "kind": "function", "doc": "

Creates a Sequential model instance.

\n\n

Args:\n layers: Optional list of layers to add to the model.\n name: Optional name for the model.

\n", "signature": "()"}, "target_models.KaggleModel": {"fullname": "target_models.KaggleModel", "modulename": "target_models", "qualname": "KaggleModel", "kind": "class", "doc": "

On the purchase dataset (see Section VI-A), we train a fully\nconnected neural network with one hidden layer of size 128\nand a SoftMax layer. We use Tanh as the activation function.\n\u2018We set the learning rate to 0.001, the learning rate decay to\n1e \u2014 07, and the maximum epochs of training to 200.

\n", "bases": "keras.engine.sequential.Sequential"}, "target_models.KaggleModel.__init__": {"fullname": "target_models.KaggleModel.__init__", "modulename": "target_models", "qualname": "KaggleModel.__init__", "kind": "function", "doc": "

Creates a Sequential model instance.

\n\n

Args:\n layers: Optional list of layers to add to the model.\n name: Optional name for the model.

\n", "signature": "(output_size: int)"}, "target_models.load_model": {"fullname": "target_models.load_model", "modulename": "target_models", "qualname": "load_model", "kind": "function", "doc": "

Load model from disk.

\n\n

The file name will be constructed from the name argument.

\n", "signature": "(name: str, verbose=True) -> keras.engine.sequential.Sequential:", "funcdef": "def"}, "target_models.save_model": {"fullname": "target_models.save_model", "modulename": "target_models", "qualname": "save_model", "kind": "function", "doc": "

Save model to disk.

\n\n

The file name will be constructed from the name argument.

\n", "signature": "(name: str, model: keras.engine.sequential.Sequential) -> None:", "funcdef": "def"}, "target_models.train_model": {"fullname": "target_models.train_model", "modulename": "target_models", "qualname": "train_model", "kind": "function", "doc": "

\n", "signature": "(\tmodel: keras.engine.sequential.Sequential,\tmodelName: str,\ttrainData: tensorflow.python.data.ops.dataset_ops.DatasetV2,\ttestData: tensorflow.python.data.ops.dataset_ops.DatasetV2,\thyperpar: Dict):", "funcdef": "def"}, "target_models.evaluate_model": {"fullname": "target_models.evaluate_model", "modulename": "target_models", "qualname": "evaluate_model", "kind": "function", "doc": "

\n", "signature": "(\tmodel: keras.engine.sequential.Sequential,\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2):", "funcdef": "def"}, "target_models.get_model_name": {"fullname": "target_models.get_model_name", "modulename": "target_models", "qualname": "get_model_name", "kind": "function", "doc": "

\n", "signature": "(config: Dict) -> str:", "funcdef": "def"}, "target_models.get_target_model": {"fullname": "target_models.get_target_model", "modulename": "target_models", "qualname": "get_target_model", "kind": "function", "doc": "

Try to load target model. If it doesn't work, train it.

\n", "signature": "(config: Dict, targetDataset) -> keras.engine.sequential.Sequential:", "funcdef": "def"}, "target_models.train_target_model": {"fullname": "target_models.train_target_model", "modulename": "target_models", "qualname": "train_target_model", "kind": "function", "doc": "

\n", "signature": "(config: Dict, targetDataset) -> keras.engine.sequential.Sequential:", "funcdef": "def"}}, "docInfo": {"attack_data": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 47}, "attack_data.set_seed": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 17, "bases": 0, "doc": 17}, "attack_data.from_target_data": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 146, "bases": 0, "doc": 95}, "attack_data.load_attack_data": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 88, "bases": 0, "doc": 3}, "attack_data.save": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 60, "bases": 0, "doc": 3}, "attack_data.shuffle": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 83, "bases": 0, "doc": 3}, "attack_data.balance_attack_data": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 84, "bases": 0, "doc": 14}, "attack_data.split_dataset": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 54, "bases": 0, "doc": 3}, "attack_data.split_attack_data_for_training": {"qualname": 5, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 60, "bases": 0, "doc": 3}, "attack_data.get_attack_data": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 199, "bases": 0, "doc": 27}, "attack_data.from_shadow_models": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 163, "bases": 0, "doc": 26}, "attack_model": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "attack_model.set_seed": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 17, "bases": 0, "doc": 17}, "attack_model.KaggleAttackModel": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, 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Constructing training data for attack models

\n\n

Membership status is one-hot encoded with the following scheme:

\n\n

[1,0] or 0 : respective record was part of training data\n[0,1] or 1 : respective record was not part of training data

\n"}, "attack_data.set_seed": {"fullname": "attack_data.set_seed", "modulename": "attack_data", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "attack_data.from_target_data": {"fullname": "attack_data.from_target_data", "modulename": "attack_data", "qualname": "from_target_data", "kind": "function", "doc": "

Use the training and test data to construct the attack dataset.\nSplitting is done according to this scheme:

\n\n

Training data Testing data

\n\n

\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 \u2502 \u2502 \u2502 \u2502 \u2502\n\u2502 B \u2502 C \u2502 \u2502 A \u2502 D \u2502\n\u2514\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2518 \u2514\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2518\n \u2502 \u2502 \u2502 \u2502\n \u2502 \u250c\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2502\n B \u2502 A \u2502 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u2502\n \u2502 \u2502 \u2502C \u2502D\n \u2502 \u2502 \u2502 \u2502\n \u25bc \u25bc \u25bc \u25bc

\n\n

\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510 \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 \u2502 \u2502 \u2502\n\u2502 A+B \u2502 \u2502 C+D \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518 \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\nAttack train data Attack test data

\n", "signature": "(\ttargetTrainData: tensorflow.python.data.ops.dataset_ops.DatasetV2,\ttargetTestData: tensorflow.python.data.ops.dataset_ops.DatasetV2,\ttargetModel: keras.engine.sequential.Sequential,\tlabel: int) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "attack_data.load_attack_data": {"fullname": "attack_data.load_attack_data", "modulename": "attack_data", "qualname": "load_attack_data", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict) -> List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]:", "funcdef": "def"}, "attack_data.save": {"fullname": "attack_data.save", "modulename": "attack_data", "qualname": "save", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict,\tdatasets: List[tensorflow.python.data.ops.dataset_ops.DatasetV2]):", "funcdef": "def"}, "attack_data.shuffle": {"fullname": "attack_data.shuffle", "modulename": "attack_data", "qualname": "shuffle", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2,\tbufferSize=10000) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "attack_data.balance_attack_data": {"fullname": "attack_data.balance_attack_data", "modulename": "attack_data", "qualname": "balance_attack_data", "kind": "function", "doc": "

Make sure that input datasets have equal number of in/out datapoints.

\n", "signature": "(\tdatasets: List[tensorflow.python.data.ops.dataset_ops.DatasetV2]) -> List[tensorflow.python.data.ops.dataset_ops.DatasetV2]:", "funcdef": "def"}, "attack_data.split_dataset": {"fullname": "attack_data.split_dataset", "modulename": "attack_data", "qualname": "split_dataset", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2,\tsplit: float):", "funcdef": "def"}, "attack_data.split_attack_data_for_training": {"fullname": "attack_data.split_attack_data_for_training", "modulename": "attack_data", "qualname": "split_attack_data_for_training", "kind": "function", "doc": "

\n", "signature": "(\tdatasets: List[tensorflow.python.data.ops.dataset_ops.DatasetV2],\tconfig: Dict):", "funcdef": "def"}, "attack_data.get_attack_data": {"fullname": "attack_data.get_attack_data", "modulename": "attack_data", "qualname": "get_attack_data", "kind": "function", "doc": "

This function predicts and then labels the provided datasets on their\nrespective shadow model, thus creating the labeled data needed for the\nattack model.

\n", "signature": "(\tconfig: Dict,\tshadowModels: List[keras.engine.sequential.Sequential],\tshadowDatasets: List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]) -> List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]:", "funcdef": "def"}, "attack_data.from_shadow_models": {"fullname": "attack_data.from_shadow_models", "modulename": "attack_data", "qualname": "from_shadow_models", "kind": "function", "doc": "

Predicts the shadow data on the shadow models themselves and labels it with\n\"in\" and \"out\", for the attack model to train on.

\n", "signature": "(\tconfig: Dict,\tshadowModels: List[keras.engine.sequential.Sequential],\tshadowDatasets: List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]) -> List[tensorflow.python.data.ops.dataset_ops.DatasetV2]:", "funcdef": "def"}, "attack_model": {"fullname": "attack_model", "modulename": "attack_model", "kind": "module", "doc": "

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Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "attack_model.KaggleAttackModel": {"fullname": "attack_model.KaggleAttackModel", "modulename": "attack_model", "qualname": "KaggleAttackModel", "kind": "class", "doc": "

Architecture:\n Fully connected NN,\n 1 hiddenlayer, size 64,\n ReLU activation\n Softmax LAyer

\n\n

One model for each class

\n", "bases": "keras.engine.sequential.Sequential"}, "attack_model.KaggleAttackModel.__init__": {"fullname": "attack_model.KaggleAttackModel.__init__", "modulename": "attack_model", "qualname": "KaggleAttackModel.__init__", "kind": "function", "doc": "

Creates a Sequential model instance.

\n\n

Args:\n layers: Optional list of layers to add to the model.\n name: Optional name for the model.

\n", "signature": "(numClasses: int)"}, "attack_model.load_model": {"fullname": "attack_model.load_model", "modulename": "attack_model", "qualname": "load_model", "kind": "function", "doc": "

Load model from disk.

\n\n

The file name will be constructed from the name argument.

\n", "signature": "(name: str, verbose=True) -> keras.engine.sequential.Sequential:", "funcdef": "def"}, "attack_model.save_model": {"fullname": "attack_model.save_model", "modulename": "attack_model", "qualname": "save_model", "kind": "function", "doc": "

Save model to disk.

\n\n

The file name will be constructed from the name argument.

\n", "signature": "(name: str, model: keras.engine.sequential.Sequential) -> None:", "funcdef": "def"}, "attack_model.train_model": {"fullname": "attack_model.train_model", "modulename": "attack_model", "qualname": "train_model", "kind": "function", "doc": "

\n", "signature": "(\tmodel: keras.engine.sequential.Sequential,\tmodelName: str,\ttrainData: tensorflow.python.data.ops.dataset_ops.DatasetV2,\ttestData: tensorflow.python.data.ops.dataset_ops.DatasetV2,\thyperpar: Dict):", "funcdef": "def"}, "attack_model.evaluate_models": {"fullname": "attack_model.evaluate_models", "modulename": "attack_model", "qualname": "evaluate_models", "kind": "function", "doc": "

\n", "signature": "(\tmodels: List[keras.engine.sequential.Sequential],\tdatasets: List[tensorflow.python.data.ops.dataset_ops.DatasetV2]) -> float:", "funcdef": "def"}, "attack_model.evaluate_model": {"fullname": "attack_model.evaluate_model", "modulename": "attack_model", "qualname": "evaluate_model", "kind": "function", "doc": "

\n", "signature": "(\tmodel: keras.engine.sequential.Sequential,\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2):", "funcdef": "def"}, "attack_model.get_model_name": {"fullname": "attack_model.get_model_name", "modulename": "attack_model", "qualname": "get_model_name", "kind": "function", "doc": "

\n", "signature": "(config: Dict, i: int) -> str:", "funcdef": "def"}, "attack_model.get_attack_models": {"fullname": "attack_model.get_attack_models", "modulename": "attack_model", "qualname": "get_attack_models", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict,\tattackDatasets: List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]) -> List[attack_model.KaggleAttackModel]:", "funcdef": "def"}, "attack_pipeline": {"fullname": "attack_pipeline", "modulename": "attack_pipeline", "kind": "module", "doc": "

\n"}, "attack_pipeline.set_seed": {"fullname": "attack_pipeline.set_seed", "modulename": "attack_pipeline", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "attack_pipeline.load_target_data": {"fullname": "attack_pipeline.load_target_data", "modulename": "attack_pipeline", "qualname": "load_target_data", "kind": "function", "doc": "

Returns tuple trainData, restData.

\n\n

RestData is data unused for training and testing previously.

\n", "signature": "(\tconfig: Dict) -> Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]:", "funcdef": "def"}, "attack_pipeline.run_pipeline": {"fullname": "attack_pipeline.run_pipeline", "modulename": "attack_pipeline", "qualname": "run_pipeline", "kind": "function", "doc": "

\n", "signature": "(targetModel, targetTrainData, targetRestData):", "funcdef": "def"}, "attack_pipeline.process_results": {"fullname": "attack_pipeline.process_results", "modulename": "attack_pipeline", "qualname": "process_results", "kind": "function", "doc": "

\n", "signature": "(precision, recall, precisionPerClass, recallPerClass):", "funcdef": "def"}, "configuration": {"fullname": "configuration", "modulename": "configuration", "kind": "module", "doc": "

This module allows the parsing of YAML config files into a python dict.

\n\n

To load a YAML file located in config/ use the method from_name:

\n\n
\n
config = configuration.from_name("example.yml")\n
\n
\n\n

Access the information of the YAML file by accessing members of the returned\nobject. E.g. if the YAML file has an entry \"seed: 1234\":

\n\n
\n
print(config["seed"])\n1234\n
\n
\n\n

You can also load from a relative or absolute file path:

\n\n
\n
config_relative = configuration.from_rel_path("../example.yml")\nconfig_absolute = configuration.from_abs_path("/home/user/config/example.yml")\n
\n
\n\n

Save the current configuration to config/ using the save_to_file method:

\n\n
\n
save_to_file(config) # Uses current time as filename\nsave_to_file(config,"example.yaml") \n
\n
\n"}, "configuration.save_to_file": {"fullname": "configuration.save_to_file", "modulename": "configuration", "qualname": "save_to_file", "kind": "function", "doc": "

If a configName is passed, this will be used to construct the file\nname. Otherwise the current time is used. Either way, config files are\nwritten into the config/ directory.

\n", "signature": "(configuration: Dict, configName: Optional[str] = None):", "funcdef": "def"}, "configuration.from_abs_path": {"fullname": "configuration.from_abs_path", "modulename": "configuration", "qualname": "from_abs_path", "kind": "function", "doc": "

Load a Configuration from absolute file path.

\n", "signature": "(absoluteFilePath: str) -> Dict:", "funcdef": "def"}, "configuration.from_rel_path": {"fullname": "configuration.from_rel_path", "modulename": "configuration", "qualname": "from_rel_path", "kind": "function", "doc": "

Load a Configuration from relative file path.

\n", "signature": "(fileName: str) -> Dict:", "funcdef": "def"}, "configuration.from_name": {"fullname": "configuration.from_name", "modulename": "configuration", "qualname": "from_name", "kind": "function", "doc": "

Load a Configuration from config/.

\n\n

fileName is the name of the file inside config/.

\n", "signature": "(fileName: str) -> Dict:", "funcdef": "def"}, "configuration.from_cli_options": {"fullname": "configuration.from_cli_options", "modulename": "configuration", "qualname": "from_cli_options", "kind": "function", "doc": "

Take options from CLI and load correct config file.

\n", "signature": "(options: Dict) -> Dict:", "funcdef": "def"}, "datasets": {"fullname": "datasets", "modulename": "datasets", "kind": "module", "doc": "

Loading datasets

\n\n

This module preprocesses the datasets (where necessary) and returns them as \ntf.data.Dataset objects.

\n\n

The main interface is the function load_dataset(datasetName:str). It loads a\ntf.data.Dataset from disk and if none is found, creates one and saves it to\ndisk. That means, it can also be used in advance to ensure that the dataset is\nstored to disk and can be quickly loaded during training.

\n\n

Use it like this:

\n\n
\n
kaggle = load_dataset("kaggle")\n
\n
\n\n

Valid datasetName values are: \"cifar10\", \"cifar100\", \"kaggle\", \"kaggle_2\",\n\"kaggle_10\",\"kaggle_20\",\"kaggle_50\",\"kaggle_100\". Using the key \"kaggle_x\" \nreturns the Kaggle dataset, but clustered into x classes. If the dataset has \nto be constructed, a k-means clustering is done in the backend, so it can take \na while.

\n\n

Shuffling

\n\n

To shuffle a Kaggle dataset (e.g. to construct a training set from random \nsamples) call shuffle_kaggle:

\n\n
\n
kaggle = load_dataset("kaggle")\nkaggle_shuffled = shuffle_kaggle(kaggle)\n
\n
\n\n

Pre-loading for later use

\n\n

There is a conveniance method load_all_datasets, which loads all datasets as\ntf.data.Dataset and saves them to disk, so that they can be quickly loaded in\nthe future.

\n\n

Seeding

\n\n

Use set_seed to set a global seed, used by all random functions:

\n\n
\n
set_seed(1234)\n
\n
\n"}, "datasets.set_seed": {"fullname": "datasets.set_seed", "modulename": "datasets", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "datasets.shuffle": {"fullname": "datasets.shuffle", "modulename": "datasets", "qualname": "shuffle", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "datasets.load_attack": {"fullname": "datasets.load_attack", "modulename": "datasets", "qualname": "load_attack", "kind": "function", "doc": "

\n", "signature": "(\tdatasetName: str,\tverbose=True) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "datasets.load_shadow": {"fullname": "datasets.load_shadow", "modulename": "datasets", "qualname": "load_shadow", "kind": "function", "doc": "

\n", "signature": "(\tdatasetName: str,\tverbose=True) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "datasets.load_numpy_array": {"fullname": "datasets.load_numpy_array", "modulename": "datasets", "qualname": "load_numpy_array", "kind": "function", "doc": "

\n", "signature": "(filename: str):", "funcdef": "def"}, "datasets.save_numpy_array": {"fullname": "datasets.save_numpy_array", "modulename": "datasets", "qualname": "save_numpy_array", "kind": "function", "doc": "

\n", "signature": "(filename: str, array):", "funcdef": "def"}, "datasets.save_target": {"fullname": "datasets.save_target", "modulename": "datasets", "qualname": "save_target", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2,\tdatasetName: str):", "funcdef": "def"}, "datasets.load_target": {"fullname": "datasets.load_target", "modulename": "datasets", "qualname": "load_target", "kind": "function", "doc": "

\n", "signature": "(\tdatasetName: str,\tverbose=True) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "datasets.save_attack": {"fullname": "datasets.save_attack", "modulename": "datasets", "qualname": "save_attack", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2,\tdatasetName: str):", "funcdef": "def"}, "datasets.save_shadow": {"fullname": "datasets.save_shadow", "modulename": "datasets", "qualname": "save_shadow", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2,\tdatasetName: str):", "funcdef": "def"}, "datasets.delete_shadow": {"fullname": "datasets.delete_shadow", "modulename": "datasets", "qualname": "delete_shadow", "kind": "function", "doc": "

\n", "signature": "(datasetName: str):", "funcdef": "def"}, "datasets.load_dataset": {"fullname": "datasets.load_dataset", "modulename": "datasets", "qualname": "load_dataset", "kind": "function", "doc": "

Load a dataset.

\n\n

Valid datasetName values are: \"cifar10\", \"cifar100\", \"kaggle\", \"kaggle_2\",\n\"kaggle_10\",\"kaggle_20\",\"kaggle_50\",\"kaggle_100\".

\n", "signature": "(datasetName: str) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "datasets.split_dataset": {"fullname": "datasets.split_dataset", "modulename": "datasets", "qualname": "split_dataset", "kind": "function", "doc": "

\n", "signature": "(\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2,\tnumSubsets: int) -> List[tensorflow.python.data.ops.dataset_ops.DatasetV2]:", "funcdef": "def"}, "datasets.load_all_datasets": {"fullname": "datasets.load_all_datasets", "modulename": "datasets", "qualname": "load_all_datasets", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "download": {"fullname": "download", "modulename": "download", "kind": "module", "doc": "

This module downloads all necessary datasets.

\n\n

Usage examples:

\n\n
\n
import download\ndownload.download_cifar100()\ndownload.download_cifar10()\ndownload.download_kaggle()\ndownload.download_all_datasets() # Download all 3 at once\n
\n
\n"}, "download.download_kaggle": {"fullname": "download.download_kaggle", "modulename": "download", "qualname": "download_kaggle", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "download.download_cifar10": {"fullname": "download.download_cifar10", "modulename": "download", "qualname": "download_cifar10", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "download.download_cifar100": {"fullname": "download.download_cifar100", "modulename": "download", "qualname": "download_cifar100", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "download.download_all_datasets": {"fullname": "download.download_all_datasets", "modulename": "download", "qualname": "download_all_datasets", "kind": "function", "doc": "

\n", "signature": "():", "funcdef": "def"}, "download.download_dataset": {"fullname": "download.download_dataset", "modulename": "download", "qualname": "download_dataset", "kind": "function", "doc": "

\n", "signature": "(datasetName: str):", "funcdef": "def"}, "main": {"fullname": "main", "modulename": "main", "kind": "module", "doc": "

\n"}, "main.parse_args": {"fullname": "main.parse_args", "modulename": "main", "qualname": "parse_args", "kind": "function", "doc": "

\n", "signature": "() -> Dict:", "funcdef": "def"}, "main.run_script": {"fullname": "main.run_script", "modulename": "main", "qualname": "run_script", "kind": "function", "doc": "

\n", "signature": "(scriptName: str, configFile: str):", "funcdef": "def"}, "main.prepare": {"fullname": "main.prepare", "modulename": "main", "qualname": "prepare", "kind": "function", "doc": "

\n", "signature": "(configFile: str):", "funcdef": "def"}, "main.evaluate": {"fullname": "main.evaluate", "modulename": "main", "qualname": "evaluate", "kind": "function", "doc": "

\n", "signature": "(config: Dict):", "funcdef": "def"}, "shadow_data": {"fullname": "shadow_data", "modulename": "shadow_data", "kind": "module", "doc": "

This module generates shadow data for training the shadow models.\nIt assumes varying knowledge about the original dataset, depending on the\nattacker capabilities.

\n"}, "shadow_data.set_seed": {"fullname": "shadow_data.set_seed", "modulename": "shadow_data", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "shadow_data.generate_shadow_data_sampling": {"fullname": "shadow_data.generate_shadow_data_sampling", "modulename": "shadow_data", "qualname": "generate_shadow_data_sampling", "kind": "function", "doc": "

Generate synthetic data for the shadow models by randomly sampling data\npoints from the original data set.

\n", "signature": "(\toriginal_data: tensorflow.python.data.ops.dataset_ops.DatasetV2) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "shadow_data.split_shadow_data": {"fullname": "shadow_data.split_shadow_data", "modulename": "shadow_data", "qualname": "split_shadow_data", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict,\tshadowData: tensorflow.python.data.ops.dataset_ops.DatasetV2) -> List[tensorflow.python.data.ops.dataset_ops.DatasetV2]:", "funcdef": "def"}, "shadow_data.load_shadow_data": {"fullname": "shadow_data.load_shadow_data", "modulename": "shadow_data", "qualname": "load_shadow_data", "kind": "function", "doc": "

\n", "signature": "(config: Dict):", "funcdef": "def"}, "shadow_data.get_shadow_data_name": {"fullname": "shadow_data.get_shadow_data_name", "modulename": "shadow_data", "qualname": "get_shadow_data_name", "kind": "function", "doc": "

\n", "signature": "(config: Dict):", "funcdef": "def"}, "shadow_data.get_shadow_data": {"fullname": "shadow_data.get_shadow_data", "modulename": "shadow_data", "qualname": "get_shadow_data", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict,\ttargetDataset,\ttargetModel) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "shadow_data.generate_shadow_data_noisy": {"fullname": "shadow_data.generate_shadow_data_noisy", "modulename": "shadow_data", "qualname": "generate_shadow_data_noisy", "kind": "function", "doc": "

Generate synthetic data for the shadow models by using a noisy version of\nthe original data.\nReturns only the noisy data, no the oririnal data.

\n\n

Arguments:\n fraction: percentage of labels that will be flipped per data record to\n make it \"noisy\"

\n", "signature": "(\toriginal_data: tensorflow.python.data.ops.dataset_ops.DatasetV2,\toutputSize: int,\tfraction: float = 0.1) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "shadow_data.generate_shadow_data_statistic": {"fullname": "shadow_data.generate_shadow_data_statistic", "modulename": "shadow_data", "qualname": "generate_shadow_data_statistic", "kind": "function", "doc": "

Generate synthetic data for the shadow models by using the marginal\ndistribution of features in the original dataset.

\n", "signature": "(\tconfig: Dict,\t**hyperpars) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "shadow_data.hill_climbing": {"fullname": "shadow_data.hill_climbing", "modulename": "shadow_data", "qualname": "hill_climbing", "kind": "function", "doc": "

Generate synthetic data for the shadow models by querying the target model\nfor randomly sampled records, in order to find those that are classified\nwith high confidence.

\n\n

numRecords: size of generated dataset\nhyperpars has the following keys (taken from the paper:\nk_max,k_min,rej_max,conf_min,iter_max)

\n", "signature": "(\ttargetModel: keras.engine.sequential.Sequential,\tnumRecords: int,\t**hyperpars) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "shadow_data.get_target_model_rest_data": {"fullname": "shadow_data.get_target_model_rest_data", "modulename": "shadow_data", "qualname": "get_target_model_rest_data", "kind": "function", "doc": "

\n", "signature": "(config: Dict) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:", "funcdef": "def"}, "shadow_models": {"fullname": "shadow_models", "modulename": "shadow_models", "kind": "module", "doc": "

\n"}, "shadow_models.set_seed": {"fullname": "shadow_models.set_seed", "modulename": "shadow_models", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "shadow_models.get_shadow_model_name": {"fullname": "shadow_models.get_shadow_model_name", "modulename": "shadow_models", "qualname": "get_shadow_model_name", "kind": "function", "doc": "

\n", "signature": "(config: Dict, i: int):", "funcdef": "def"}, "shadow_models.load_shadow_models_and_datasets": {"fullname": "shadow_models.load_shadow_models_and_datasets", "modulename": "shadow_models", "qualname": "load_shadow_models_and_datasets", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict) -> Tuple[List[keras.engine.sequential.Sequential], List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]]:", "funcdef": "def"}, "shadow_models.train_shadow_models": {"fullname": "shadow_models.train_shadow_models", "modulename": "shadow_models", "qualname": "train_shadow_models", "kind": "function", "doc": "

\n", "signature": "(\tconfig: Dict,\tshadowDatasets: List[tensorflow.python.data.ops.dataset_ops.DatasetV2]) -> Tuple[List[keras.engine.sequential.Sequential], List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]]:", "funcdef": "def"}, "shadow_models.get_shadow_models_and_datasets": {"fullname": "shadow_models.get_shadow_models_and_datasets", "modulename": "shadow_models", "qualname": "get_shadow_models_and_datasets", "kind": "function", "doc": "

Tries to load shadow datasets from disk, alternatively trains from scratch.

\n\n

Returns 2 lists:\n models: the trained shadow models and a list of tuples, containing\n datasets: the training and test data for the corresponding shadow models

\n\n
E.g. models[0] is trained with datasets[0,0] and tested on datasets[0,1]\n
\n", "signature": "(\tconfig: Dict,\tshadowDatasets: List[tensorflow.python.data.ops.dataset_ops.DatasetV2]) -> Tuple[List[keras.engine.sequential.Sequential], List[Tuple[tensorflow.python.data.ops.dataset_ops.DatasetV2, tensorflow.python.data.ops.dataset_ops.DatasetV2]]]:", "funcdef": "def"}, "target_models": {"fullname": "target_models", "modulename": "target_models", "kind": "module", "doc": "

This module defines the victim models and provides methods to save, load and\ntrain them.

\n\n

To set the seed:

\n\n
\n
victim_models.set_seed(1234)\n
\n
\n\n

To load the models:

\n\n
\n
cifar10Model = victim_models.CifarModel()\nkaggleModel = victim_models.KaggleModel()\n
\n
\n\n

To train it with a tf.data.Dataset called cifar10 (dimension has to fit):

\n\n
\n
victim_models.train_model(cifar10Model, cifar10, epochs=1)\n
\n
\n\n

To save and load the model:

\n\n
\n
victim_models.save_model("filename",cifar10Model)\ncifar10Model = victim_models.load_model("filename")\n
\n
\n"}, "target_models.set_seed": {"fullname": "target_models.set_seed", "modulename": "target_models", "qualname": "set_seed", "kind": "function", "doc": "

Set the global seed that will be used for all functions that include\nrandomness.

\n", "signature": "(new_seed: int):", "funcdef": "def"}, "target_models.CifarModel": {"fullname": "target_models.CifarModel", "modulename": "target_models", "qualname": "CifarModel", "kind": "class", "doc": "

On CIFAR datasets, we train a standard convolutional neural\nnetwork (CNN) with two convolution and max pooling layers\nplus a fully connected layer of size 128 and a Sof tMax layer.\n\u2018We use Tanh as the activation function. We set the learning\nrate to 0.001, the learning rate decay to le \u2014 07, and the\nmaximum epochs of training to 100.

\n", "bases": "keras.engine.sequential.Sequential"}, "target_models.CifarModel.__init__": {"fullname": "target_models.CifarModel.__init__", "modulename": "target_models", "qualname": "CifarModel.__init__", "kind": "function", "doc": "

Creates a Sequential model instance.

\n\n

Args:\n layers: Optional list of layers to add to the model.\n name: Optional name for the model.

\n", "signature": "()"}, "target_models.KaggleModel": {"fullname": "target_models.KaggleModel", "modulename": "target_models", "qualname": "KaggleModel", "kind": "class", "doc": "

On the purchase dataset (see Section VI-A), we train a fully\nconnected neural network with one hidden layer of size 128\nand a SoftMax layer. We use Tanh as the activation function.\n\u2018We set the learning rate to 0.001, the learning rate decay to\n1e \u2014 07, and the maximum epochs of training to 200.

\n", "bases": "keras.engine.sequential.Sequential"}, "target_models.KaggleModel.__init__": {"fullname": "target_models.KaggleModel.__init__", "modulename": "target_models", "qualname": "KaggleModel.__init__", "kind": "function", "doc": "

Creates a Sequential model instance.

\n\n

Args:\n layers: Optional list of layers to add to the model.\n name: Optional name for the model.

\n", "signature": "(output_size: int)"}, "target_models.load_model": {"fullname": "target_models.load_model", "modulename": "target_models", "qualname": "load_model", "kind": "function", "doc": "

Load model from disk.

\n\n

The file name will be constructed from the name argument.

\n", "signature": "(name: str, verbose=True) -> keras.engine.sequential.Sequential:", "funcdef": "def"}, "target_models.save_model": {"fullname": "target_models.save_model", "modulename": "target_models", "qualname": "save_model", "kind": "function", "doc": "

Save model to disk.

\n\n

The file name will be constructed from the name argument.

\n", "signature": "(name: str, model: keras.engine.sequential.Sequential) -> None:", "funcdef": "def"}, "target_models.train_model": {"fullname": "target_models.train_model", "modulename": "target_models", "qualname": "train_model", "kind": "function", "doc": "

\n", "signature": "(\tmodel: keras.engine.sequential.Sequential,\tmodelName: str,\ttrainData: tensorflow.python.data.ops.dataset_ops.DatasetV2,\ttestData: tensorflow.python.data.ops.dataset_ops.DatasetV2,\thyperpar: Dict):", "funcdef": "def"}, "target_models.evaluate_model": {"fullname": "target_models.evaluate_model", "modulename": "target_models", "qualname": "evaluate_model", "kind": "function", "doc": "

\n", "signature": "(\tmodel: keras.engine.sequential.Sequential,\tdataset: tensorflow.python.data.ops.dataset_ops.DatasetV2):", "funcdef": "def"}, "target_models.get_model_name": {"fullname": "target_models.get_model_name", "modulename": "target_models", "qualname": "get_model_name", "kind": "function", "doc": "

\n", "signature": "(config: Dict) -> str:", "funcdef": "def"}, "target_models.get_target_model": {"fullname": "target_models.get_target_model", "modulename": "target_models", "qualname": "get_target_model", "kind": "function", "doc": "

Try to load target model. If it doesn't work, train it.

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16environ["TF_CPP_MIN_LOG_LEVEL"] = "2" # NOQA 17 18import tensorflow as tf - 19from tensorflow.python.framework import random_seed - 20from tensorflow.data import Dataset # pyright: ignore - 21from tensorflow.keras import Sequential # pyright: ignore - 22 - 23global_seed: int = 1234 - 24globalRandomGen = np.random.default_rng(global_seed) - 25 + 19from tensorflow.keras.utils import to_categorical # pyright: ignore + 20from tensorflow.python.framework import random_seed + 21from tensorflow.data import Dataset # pyright: ignore + 22from tensorflow.keras import Sequential # pyright: ignore + 23 + 24global_seed: int = 1234 + 25globalRandomGen = np.random.default_rng(global_seed) 26 - 27def set_seed(new_seed: int): - 28 """ - 29 Set the global seed that will be used for all functions that include - 30 randomness. - 31 """ - 32 global global_seed - 33 global_seed = new_seed - 34 np.random.seed(global_seed) - 35 random.seed(global_seed) - 36 random_seed.set_seed(global_seed) - 37 + 27 + 28def set_seed(new_seed: int): + 29 """ + 30 Set the global seed that will be used for all functions that include + 31 randomness. + 32 """ + 33 global global_seed + 34 global_seed = new_seed + 35 np.random.seed(global_seed) + 36 random.seed(global_seed) + 37 random_seed.set_seed(global_seed) 38 - 39def generate_shadow_data_sampling(original_data: Dataset) -> Dataset: - 40 """ - 41 Generate synthetic data for the shadow models by randomly sampling data - 42 points from the original data set. - 43 """ - 44 sample_dataset: Dataset = tf.data.Dataset.sample_from_datasets( - 45 [original_data], seed=global_seed, stop_on_empty_dataset=True) - 46 return sample_dataset - 47 + 39 + 40def generate_shadow_data_sampling(original_data: Dataset) -> Dataset: + 41 """ + 42 Generate synthetic data for the shadow models by randomly sampling data + 43 points from the original data set. + 44 """ + 45 sample_dataset: Dataset = tf.data.Dataset.sample_from_datasets( + 46 [original_data], seed=global_seed, stop_on_empty_dataset=True) + 47 return sample_dataset 48 - 49def split_shadow_data(config: Dict, shadowData: ds.Dataset) -> List[ds.Dataset]: - 50 print("Splitting shadow data into subsets.") - 51 numSubsets = config["shadowModels"]["number"] - 52 return ds.split_dataset(shadowData, numSubsets) - 53 + 49 + 50def split_shadow_data(config: Dict, shadowData: ds.Dataset) -> List[ds.Dataset]: + 51 print("Splitting shadow data into subsets.") + 52 numSubsets = config["shadowModels"]["number"] + 53 return ds.split_dataset(shadowData, numSubsets) 54 - 55def load_shadow_data(config: Dict): - 56 dataName = get_shadow_data_name(config) - 57 return ds.load_shadow(dataName, verbose=config["verbose"]) - 58 + 55 + 56def load_shadow_data(config: Dict): + 57 dataName = get_shadow_data_name(config) + 58 return ds.load_shadow(dataName, verbose=config["verbose"]) 59 - 60def get_shadow_data_name(config: Dict): - 61 shadowConfig = config["shadowDataset"] - 62 method = shadowConfig["method"] - 63 targetDataName = config["targetDataset"]["name"] - 64 dataSize = shadowConfig["size"] - 65 hyperpars = shadowConfig[method]["hyperparameters"] - 66 if method == "noisy": - 67 dataName = f'{method}_fraction_{hyperpars["fraction"]}_size_{dataSize}_target_{targetDataName}' - 68 elif method == "hill_climbing": - 69 dataName = \ - 70 f'{method}_' + \ - 71 f'{targetDataName}_' + \ - 72 f'kmax_{hyperpars["k_max"]}_' + \ - 73 f'kmin_{hyperpars["k_min"]}_' + \ - 74 f'confmin_{hyperpars["conf_min"]}_' + \ - 75 f'rejmax_{hyperpars["rej_max"]}_' + \ - 76 f'itermax_{hyperpars["iter_max"]}_' + \ - 77 f'size_{dataSize}' - 78 elif method == "original": - 79 dataName = \ - 80 f'{method}_' + \ - 81 f'{targetDataName}_' + \ - 82 f'original_data_' + \ - 83 f'size_{dataSize}' - 84 else: - 85 raise ValueError(f"{method} is not a valid shadow data method.") - 86 return dataName - 87 - 88 - 89def get_shadow_data(config: Dict, targetDataset, targetModel) -> ds.Dataset: - 90 verbose = config["verbose"] - 91 shadowConfig = config["shadowDataset"] - 92 method = shadowConfig["method"] - 93 dataSize = shadowConfig["size"] - 94 hyperpars = shadowConfig[method]["hyperparameters"] - 95 dataName = get_shadow_data_name(config) - 96 - 97 try: - 98 print("Loading shadow data from disk.") - 99 shadowData = load_shadow_data(config) -100 except BaseException: -101 print("Loading failed, generating shadow data.") -102 -103 if method == "noisy": -104 shadowData = generate_shadow_data_noisy(targetDataset, dataSize, **hyperpars) -105 elif method == "hill_climbing": -106 shadowData = hill_climbing(targetModel, dataSize, **hyperpars) -107 elif method == "original": -108 modelName = tm.get_model_name(config) -109 restDataName = modelName + "_rest_data" -110 shadowData = ds.load_target(restDataName).take(dataSize) -111 else: -112 raise ValueError(f"{method} is not a valid shadow data method.") -113 -114 if verbose: -115 print(f"Saving shadow data {dataName} to disk.") -116 try: -117 ds.save_shadow(shadowData, dataName) -118 except BaseException: -119 print(f"Failed to save shadow data {dataName} to disk.") -120 ds.delete_shadow(dataName) -121 raise + 60 + 61def get_shadow_data_name(config: Dict): + 62 shadowConfig = config["shadowDataset"] + 63 method = shadowConfig["method"] + 64 targetDataName = config["targetDataset"]["name"] + 65 dataSize = shadowConfig["size"] + 66 hyperpars = shadowConfig[method]["hyperparameters"] + 67 if method == "noisy": + 68 dataName = f'{method}_fraction_{hyperpars["fraction"]}_size_{dataSize}_target_{targetDataName}' + 69 elif method == "hill_climbing": + 70 dataName = \ + 71 f'{method}_' + \ + 72 f'{targetDataName}_' + \ + 73 f'kmax_{hyperpars["k_max"]}_' + \ + 74 f'kmin_{hyperpars["k_min"]}_' + \ + 75 f'confmin_{hyperpars["conf_min"]}_' + \ + 76 f'rejmax_{hyperpars["rej_max"]}_' + \ + 77 f'itermax_{hyperpars["iter_max"]}_' + \ + 78 f'size_{dataSize}' + 79 elif method == "original": + 80 dataName = \ + 81 f'{method}_' + \ + 82 f'{targetDataName}_' + \ + 83 f'original_data_' + \ + 84 f'size_{dataSize}' + 85 elif method == "statistic": + 86 dataName = \ + 87 f'{method}_' + \ + 88 f'{targetDataName}_' + \ + 89 f'statistic_' + \ + 90 f'size_{dataSize}' + 91 else: + 92 raise ValueError(f"{method} is not a valid shadow data method.") + 93 return dataName + 94 + 95 + 96def get_shadow_data(config: Dict, targetDataset, targetModel) -> ds.Dataset: + 97 verbose = config["verbose"] + 98 shadowConfig = config["shadowDataset"] + 99 method = shadowConfig["method"] +100 dataSize = shadowConfig["size"] +101 hyperpars = shadowConfig[method]["hyperparameters"] +102 dataName = get_shadow_data_name(config) +103 +104 try: +105 print("Loading shadow data from disk.") +106 shadowData = load_shadow_data(config) +107 except BaseException: +108 print("Loading failed, generating shadow data.") +109 +110 if method == "noisy": +111 shadowData = generate_shadow_data_noisy(targetDataset, dataSize, **hyperpars) +112 elif method == "hill_climbing": +113 shadowData = hill_climbing(targetModel, dataSize, **hyperpars) +114 elif method == "original": +115 modelName = tm.get_model_name(config) +116 restDataName = modelName + "_rest_data" +117 shadowData = ds.load_target(restDataName).take(dataSize) +118 elif method == "statistic": +119 shadowData = generate_shadow_data_statistic(config, **hyperpars) +120 else: +121 raise ValueError(f"{method} is not a valid shadow data method.") 122 -123 return shadowData -124 -125 -126def _make_data_record_noisy(features, label, fraction): -127 # TODO: numFeatures is hardcoded -128 numFeatures = 600 -129 k = int(numFeatures * fraction) -130 return _randomize_features(features, k=k).reshape(numFeatures), label +123 if verbose: +124 print(f"Saving shadow data {dataName} to disk.") +125 try: +126 ds.save_shadow(shadowData, dataName) +127 except BaseException: +128 print(f"Failed to save shadow data {dataName} to disk.") +129 ds.delete_shadow(dataName) +130 raise 131 -132 -133def _make_dataset_noisy(original_data: Dataset, fraction: float) -> Dataset: -134 """ -135 Returns new dataset, where each element has a fraction of its features -136 flipped. -137 """ -138 return original_data.map( -139 lambda x, y: -140 tf.numpy_function(func=_make_data_record_noisy, inp=(x, y, fraction), Tout=[tf.int64, tf.int64]) -141 ) -142 -143 -144def generate_shadow_data_noisy(original_data: Dataset, outputSize: int, fraction: float = 0.1) -> Dataset: -145 """ -146 Generate synthetic data for the shadow models by using a noisy version of -147 the original data. -148 Returns only the noisy data, no the oririnal data. -149 -150 Arguments: -151 fraction: percentage of labels that will be flipped per data record to -152 make it "noisy" -153 """ -154 inputSize = original_data.cardinality().numpy() -155 # Since outputSize % inputSize not always 0, we have to fill the gap with a subset -156 # of the full input data. To avoid bias, shuffle the input data. -157 noisySet = _make_dataset_noisy(ds.shuffle(original_data), fraction) +132 return shadowData +133 +134 +135def _make_data_record_noisy(features, label, fraction): +136 # TODO: numFeatures is hardcoded +137 numFeatures = 600 +138 k = int(numFeatures * fraction) +139 return _randomize_features(features, k=k).reshape(numFeatures), label +140 +141 +142def _make_dataset_noisy(original_data: Dataset, fraction: float) -> Dataset: +143 """ +144 Returns new dataset, where each element has a fraction of its features +145 flipped. +146 """ +147 return original_data.map( +148 lambda x, y: +149 tf.numpy_function(func=_make_data_record_noisy, inp=(x, y, fraction), Tout=[tf.int64, tf.int64]) +150 ) +151 +152 +153def generate_shadow_data_noisy(original_data: Dataset, outputSize: int, fraction: float = 0.1) -> Dataset: +154 """ +155 Generate synthetic data for the shadow models by using a noisy version of +156 the original data. +157 Returns only the noisy data, no the oririnal data. 158 -159 if inputSize >= outputSize: -160 return noisySet.take(outputSize) -161 -162 numNoisyVersions = int(np.floor(outputSize / inputSize)) -163 # How many records to add after collecting numNoisyVersions sets -164 offset = outputSize % inputSize -165 -166 for _ in range(numNoisyVersions - 1): -167 newNoisySet = _make_dataset_noisy(ds.shuffle(original_data), fraction) -168 noisySet = noisySet.concatenate(newNoisySet) -169 -170 offsetSet = _make_dataset_noisy( -171 ds.shuffle(original_data), fraction).take(offset) -172 return noisySet.concatenate(offsetSet) -173 +159 Arguments: +160 fraction: percentage of labels that will be flipped per data record to +161 make it "noisy" +162 """ +163 inputSize = original_data.cardinality().numpy() +164 # Since outputSize % inputSize not always 0, we have to fill the gap with a subset +165 # of the full input data. To avoid bias, shuffle the input data. +166 noisySet = _make_dataset_noisy(ds.shuffle(original_data), fraction) +167 +168 if inputSize >= outputSize: +169 return noisySet.take(outputSize) +170 +171 numNoisyVersions = int(np.floor(outputSize / inputSize)) +172 # How many records to add after collecting numNoisyVersions sets +173 offset = outputSize % inputSize 174 -175def generate_shadow_data_statistic(original_data: Dataset) -> Dataset: -176 """ -177 Generate synthetic data for the shadow models by using the marginal -178 distribution of features in the original dataset. -179 """ -180 pass -181 +175 for _ in range(numNoisyVersions - 1): +176 newNoisySet = _make_dataset_noisy(ds.shuffle(original_data), fraction) +177 noisySet = noisySet.concatenate(newNoisySet) +178 +179 offsetSet = _make_dataset_noisy( +180 ds.shuffle(original_data), fraction).take(offset) +181 return noisySet.concatenate(offsetSet) 182 -183def _generate_labels(classes: int, size: int) -> NDArray: -184 """ -185 Generate a numpy array of size `size`, where the values are integers between -186 0 and `classes` - 1, distributed as evenly as possible. -187 -188 This array will be used to generate a synthetic array of features for each -189 array element. -190 """ -191 -192 records_per_class: int = int(size / classes) -193 extra_records: int = size % classes -194 -195 labels: NDArray = np.zeros((size, 1)) -196 index: int = 0 -197 -198 for x in range(classes): -199 if x < extra_records: -200 records_for_this_class = records_per_class + 1 -201 else: -202 records_for_this_class = records_per_class -203 for y in range(records_for_this_class): -204 labels[index + y, 0] = x -205 index = index + records_for_this_class -206 -207 return labels +183def _get_filter_fn(label: int): +184 +185 wantedLabel = np.int64(label) +186 def _filter_fn(_, y): return tf.math.equal(wantedLabel, tf.math.argmax(y)) +187 return _filter_fn +188 +189def _compute_kaggle_marginals(config): +190 dataName = config["targetDataset"]["name"] +191 originalData = ds.load_dataset(dataName) +192 numFeatures = iter(originalData).get_next()[0].numpy().shape[0] +193 numLabels = iter(originalData).get_next()[1].numpy().shape[0] +194 marginalProbabilities = np.zeros((100,600)) +195 # For each class, count binary feature values to get marginal +196 for _class in range(numLabels): +197 if config["verbose"]: +198 print(f"Computing marginal probability for class {_class}/{numLabels}") +199 filteredData = originalData.filter(_get_filter_fn(_class)) +200 initialCount = np.array([0]*numFeatures) +201 countedFeatures = filteredData.reduce(initialCount, lambda oldCount, dataPoint: oldCount + dataPoint[0]).numpy() +202 sampleSize = filteredData.cardinality() +203 if sampleSize <= 0 and config["verbose"]: # tf uses constants < 0 to indicate unknown cardinality +204 sampleSize = len(list(filteredData.as_numpy_iterator())) +205 marginalProbability = countedFeatures / sampleSize +206 marginalProbabilities[_class] = marginalProbability +207 return marginalProbabilities 208 -209 -210def _randomize_features(data: NDArray, k: int, -211 numFeatures: int = 600) -> NDArray: -212 -213 featuresToFlip = random.sample(range(numFeatures), k) -214 -215 data = data.reshape((1, numFeatures)) -216 -217 data[0][featuresToFlip] ^= 1 -218 -219 return data -220 +209def generate_shadow_data_statistic(config: Dict, **hyperpars) -> Dataset: +210 """ +211 Generate synthetic data for the shadow models by using the marginal +212 distribution of features in the original dataset. +213 """ +214 # TODO: Kaggle specific +215 size = hyperpars["size"] +216 try: +217 marginalProbabilities = ds.load_numpy_array("kaggle_marginals.npy") +218 except: +219 marginalProbabilities = _compute_kaggle_marginals(config) +220 ds.save_numpy_array("kaggle_marginals.npy",marginalProbabilities) 221 -222def _get_random_record(numFeatures: int, -223 randomGenerator=globalRandomGen) -> NDArray: -224 -225 x = randomGenerator.integers(0, high=1, endpoint=True, size=numFeatures) -226 -227 return x.reshape((1, numFeatures)) +222 # Generate new records +223 numClasses = marginalProbabilities.shape[0] +224 numFeatures = marginalProbabilities.shape[1] +225 +226 features: NDArray = np.zeros((size,numFeatures)).astype(np.int32) +227 labels: NDArray = np.zeros((size,numClasses)).astype(np.int32) 228 -229 -230def _randomize_features_batched( -231 data: NDArray, k: int, batchSize: int, numFeatures: int = 600) -> NDArray: -232 -233 outputdata = np.repeat(data.reshape((numFeatures, 1)), batchSize, axis=1).transpose() -234 -235 import numpy.testing as tt -236 tt.assert_equal(outputdata[0], data.reshape(numFeatures)) -237 -238 # Flip features of the first record -239 featuresToFlip = random.sample(range(numFeatures), k) -240 outputdata[0, featuresToFlip] ^= 1 +229 recordsPerClass = int(size/numClasses) +230 +231 for _class in range(numClasses): +232 print(f"Generating records for class {_class}") +233 index_start = _class * recordsPerClass +234 index_end = (_class + 1) * recordsPerClass +235 # for index in range(index_start, index_end): +236 # labels[index] = to_categorical(_class, num_classes = numClasses) +237 labels[index_start:index_end] = np.tile(to_categorical(_class, num_classes = +238 numClasses),recordsPerClass).reshape(recordsPerClass,numClasses) +239 gen = np.random.default_rng(seed=global_seed) +240 marginalProbability = marginalProbabilities[_class] 241 -242 # Flip all further records based on the previous one -243 for i in range(1,batchSize): -244 featuresToFlip = random.sample(range(numFeatures), k) -245 outputdata[i,:] = outputdata[i-1, :] -246 outputdata[i, featuresToFlip] ^= 1 -247 -248 return outputdata -249 +242 for feature in range(numFeatures): +243 print(f"\tGenerating records for feature {feature}") +244 probability = marginalProbability[feature] +245 # sample one feature for all records in this class at once +246 sampledFeature = gen.choice([0,1], p=[1-probability,probability],size = recordsPerClass) +247 features[index_start:index_end,feature] = sampledFeature +248 +249 return Dataset.from_tensor_slices((features, labels)) 250 -251def _rebatch(x, k, batchSize, targetModel) -> Tuple[NDArray, NDArray, int]: -252 xs = _randomize_features_batched(x, k, batchSize) -253 ys = targetModel.predict(xs, batch_size=batchSize, verbose=0) -254 return xs, ys, 0 -255 -256 -257def _generate_synthetic_record_batched(label: int, -258 targetModel: Sequential, -259 k_max: int = 200, -260 k_min: int = 5, -261 conf_min: float = 0.05, -262 rej_max: int = 20, -263 iter_max: int = 200, -264 batchSize: int = 1) -> Optional[NDArray]: -265 """ -266 Synthesize a data record, using Algorithm 1 from Shokri et als -267 paper "Membership Inference Attacks against Machine Learning Models". -268 """ -269 assert label < 100 and label >= 0 -270 -271 # Initalization -272 batchIndex: int = 0 -273 numFeatures: int = 600 -274 kWasUpdated = False -275 k = k_max -276 y_c_star = 0 -277 j = 0 -278 x = _get_random_record(numFeatures) -279 haveSampled = False +251 +252 +253 +254def _generate_labels(classes: int, size: int) -> NDArray: +255 """ +256 Generate a numpy array of size `size`, where the values are integers between +257 0 and `classes` - 1, distributed as evenly as possible. +258 +259 This array will be used to generate a synthetic array of features for each +260 array element. +261 """ +262 +263 records_per_class: int = int(size / classes) +264 extra_records: int = size % classes +265 +266 labels: NDArray = np.zeros((size, 1)) +267 index: int = 0 +268 +269 for x in range(classes): +270 if x < extra_records: +271 records_for_this_class = records_per_class + 1 +272 else: +273 records_for_this_class = records_per_class +274 for y in range(records_for_this_class): +275 labels[index + y, 0] = x +276 index = index + records_for_this_class +277 +278 return labels +279 280 -281 if batchSize == 1: -282 xs = x.reshape((1, 600)) -283 ys = targetModel.predict(xs, batch_size=batchSize, verbose=0) -284 else: -285 xs, ys, batchIndex = _rebatch(x, k, batchSize, targetModel) -286 -287 # Controls number of iterations -288 for i in range(iter_max): +281def _randomize_features(data: NDArray, k: int, +282 numFeatures: int = 600) -> NDArray: +283 +284 featuresToFlip = random.sample(range(numFeatures), k) +285 +286 data = data.reshape((1, numFeatures)) +287 +288 data[0][featuresToFlip] ^= 1 289 -290 x = xs[batchIndex] -291 y = ys[batchIndex] -292 y_c = y[label] -293 predictedClass = np.argmax(y, axis=0) -294 -295 if y_c >= y_c_star: -296 if y_c > conf_min and predictedClass == label: -297 # print(f"Now sampling! {batchIndex},{y_c},{y_c_star}") -298 haveSampled = True -299 if y_c > globalRandomGen.random(): -300 return x.reshape((1, numFeatures)) -301 -302 xs, ys, batchIndex = _rebatch(x, k, batchSize, targetModel) -303 y_c_star = y_c -304 j = 0 -305 continue -306 else: -307 j = j + 1 -308 if j > rej_max and (k != k_min) and haveSampled: -309 k = int(max(k_min, np.ceil(k / 2))) -310 j = 0 -311 kWasUpdated = True +290 return data +291 +292 +293def _get_random_record(numFeatures: int, +294 randomGenerator=globalRandomGen) -> NDArray: +295 +296 x = randomGenerator.integers(0, high=1, endpoint=True, size=numFeatures) +297 +298 return x.reshape((1, numFeatures)) +299 +300 +301def _randomize_features_batched( +302 data: NDArray, k: int, batchSize: int, numFeatures: int = 600) -> NDArray: +303 +304 outputdata = np.repeat(data.reshape((numFeatures, 1)), batchSize, axis=1).transpose() +305 +306 import numpy.testing as tt +307 tt.assert_equal(outputdata[0], data.reshape(numFeatures)) +308 +309 # Flip features of the first record +310 featuresToFlip = random.sample(range(numFeatures), k) +311 outputdata[0, featuresToFlip] ^= 1 312 -313 batchExhausted = (batchIndex == batchSize - 1) -314 -315 if batchExhausted or kWasUpdated: -316 xs, ys, batchIndex = _rebatch(x, k, batchSize, targetModel) -317 kWasUpdated = False -318 else: -319 batchIndex += 1 +313 # Flip all further records based on the previous one +314 for i in range(1,batchSize): +315 featuresToFlip = random.sample(range(numFeatures), k) +316 outputdata[i,:] = outputdata[i-1, :] +317 outputdata[i, featuresToFlip] ^= 1 +318 +319 return outputdata 320 -321 # if (i % 20) == 0: -322 # print(f"{i}/{iter_max}, y_c/y_c*: {y_c:.1%}/{y_c_star:.1%}, pred/class: {predictedClass}/{label}") -323 -324 return None -325 +321 +322def _rebatch(x, k, batchSize, targetModel) -> Tuple[NDArray, NDArray, int]: +323 xs = _randomize_features_batched(x, k, batchSize) +324 ys = targetModel.predict(xs, batch_size=batchSize, verbose=0) +325 return xs, ys, 0 326 -327def _generate_synthetic_record(label: int, -328 targetModel: Sequential, -329 k_max: int = 200, -330 k_min: int = 5, -331 conf_min: float = 0.05, -332 rej_max: int = 20, -333 iter_max: int = 200, -334 batchSize: int = 1) -> Optional[NDArray]: -335 """ -336 Synthesize a data record, using Algorithm 1 from Shokri et als -337 paper "Membership Inference Attacks against Machine Learning Models". -338 """ -339 assert label < 100 and label >= 0 -340 -341 # Initalization -342 numFeatures: int = 600 -343 k = k_max -344 y_c_star = 0 -345 j = 0 -346 x = _get_random_record(numFeatures) -347 -348 # Controls number of iterations -349 for i in range(iter_max): -350 -351 y = targetModel.predict(x, batch_size=1, verbose=0) -352 y_c = y[0][label] -353 predictedClass = np.argmax(y, axis=1)[0] -354 -355 if y_c >= y_c_star: -356 if y_c > conf_min and predictedClass == label: -357 # print("Now sampling!") -358 if y_c > globalRandomGen.random(): -359 return x +327 +328def _generate_synthetic_record_batched(label: int, +329 targetModel: Sequential, +330 k_max: int = 200, +331 k_min: int = 5, +332 conf_min: float = 0.05, +333 rej_max: int = 20, +334 iter_max: int = 200, +335 batchSize: int = 1) -> Optional[NDArray]: +336 """ +337 Synthesize a data record, using Algorithm 1 from Shokri et als +338 paper "Membership Inference Attacks against Machine Learning Models". +339 """ +340 assert label < 100 and label >= 0 +341 +342 # Initalization +343 batchIndex: int = 0 +344 numFeatures: int = 600 +345 kWasUpdated = False +346 k = k_max +347 y_c_star = 0 +348 j = 0 +349 x = _get_random_record(numFeatures) +350 haveSampled = False +351 +352 if batchSize == 1: +353 xs = x.reshape((1, 600)) +354 ys = targetModel.predict(xs, batch_size=batchSize, verbose=0) +355 else: +356 xs, ys, batchIndex = _rebatch(x, k, batchSize, targetModel) +357 +358 # Controls number of iterations +359 for i in range(iter_max): 360 -361 y_c_star = y_c -362 j = 0 -363 else: -364 j = j + 1 -365 if j > rej_max and (k != k_min): -366 k = int(max(k_min, np.ceil(k / 2))) -367 j = 0 -368 -369 x = _randomize_features(x, k) # pyright: ignore -370 -371 # if (i % 20) == 0: -372 # print( -373 # f"{i}/{iter_max}, y_c/y_c*: {y_c:.1%}/{y_c_star:.1%}, pred/class: {predictedClass}/{label}") -374 -375 return None -376 -377 -378def hill_climbing(targetModel: Sequential, numRecords: int, -379 **hyperpars) -> Dataset: -380 """ -381 Generate synthetic data for the shadow models by querying the target model -382 for randomly sampled records, in order to find those that are classified -383 with high confidence. -384 -385 `numRecords`: size of generated dataset -386 `hyperpars` has the following keys (taken from the paper: -387 k_max,k_min,rej_max,conf_min,iter_max) -388 """ -389 -390 # Generate an array of labels, determining which class to synthesize for -391 # TODO: initializing and then changing `features` array might not be most -392 # efficient solution -393 -394 numClasses: int = 100 -395 labels: NDArray = _generate_labels(numClasses, numRecords) +361 x = xs[batchIndex] +362 y = ys[batchIndex] +363 y_c = y[label] +364 predictedClass = np.argmax(y, axis=0) +365 +366 if y_c >= y_c_star: +367 if y_c > conf_min and predictedClass == label: +368 # print(f"Now sampling! {batchIndex},{y_c},{y_c_star}") +369 haveSampled = True +370 if y_c > globalRandomGen.random(): +371 return x.reshape((1, numFeatures)) +372 +373 xs, ys, batchIndex = _rebatch(x, k, batchSize, targetModel) +374 y_c_star = y_c +375 j = 0 +376 continue +377 else: +378 j = j + 1 +379 if j > rej_max and (k != k_min) and haveSampled: +380 k = int(max(k_min, np.ceil(k / 2))) +381 j = 0 +382 kWasUpdated = True +383 +384 batchExhausted = (batchIndex == batchSize - 1) +385 +386 if batchExhausted or kWasUpdated: +387 xs, ys, batchIndex = _rebatch(x, k, batchSize, targetModel) +388 kWasUpdated = False +389 else: +390 batchIndex += 1 +391 +392 # if (i % 20) == 0: +393 # print(f"{i}/{iter_max}, y_c/y_c*: {y_c:.1%}/{y_c_star:.1%}, pred/class: {predictedClass}/{label}") +394 +395 return None 396 -397 numFeatures: int = 600 -398 features: NDArray = np.zeros((numRecords, numFeatures)) -399 -400 for index, label in enumerate(labels): -401 label = int(label[0]) -402 new_record = _generate_synthetic_record(label, targetModel, **hyperpars) -403 while new_record is None: -404 new_record = _generate_synthetic_record(label, targetModel, **hyperpars) -405 print(f"Generating synthetic records: {index}/{numRecords}, {index/numRecords*100:.2f}% done.") -406 features[index] = new_record.reshape((1, numFeatures)) -407 -408 features = features.reshape((numRecords, numFeatures)) -409 labels = labels.reshape((numRecords, 1)) -410 return Dataset.from_tensor_slices((features, labels)) +397 +398def _generate_synthetic_record(label: int, +399 targetModel: Sequential, +400 k_max: int = 200, +401 k_min: int = 5, +402 conf_min: float = 0.05, +403 rej_max: int = 20, +404 iter_max: int = 200, +405 batchSize: int = 1) -> Optional[NDArray]: +406 """ +407 Synthesize a data record, using Algorithm 1 from Shokri et als +408 paper "Membership Inference Attacks against Machine Learning Models". +409 """ +410 assert label < 100 and label >= 0 411 -412def get_target_model_rest_data(config:Dict) -> Dataset: -413 modelName = tm.get_model_name(config) -414 restDataName = modelName + "_rest_data" -415 return ds.load_target(restDataName) -416 -417if __name__ == "__main__": -418 import argparse -419 import configuration as con -420 import datasets as ds -421 import target_models as tm -422 -423 parser = argparse.ArgumentParser(description='Generate all the necessary shadow data and save it to disk.') -424 parser.add_argument('--config', help='Relative path to config file.',) -425 config = con.from_cli_options(vars(parser.parse_args())) -426 set_seed(config["seed"]) -427 -428 targetDataset = get_target_model_rest_data(config) -429 targetModel = tm.load_model(tm.get_model_name(config), verbose=config["verbose"]) -430 get_shadow_data(config, targetDataset, targetModel) +412 # Initalization +413 numFeatures: int = 600 +414 k = k_max +415 y_c_star = 0 +416 j = 0 +417 x = _get_random_record(numFeatures) +418 +419 # Controls number of iterations +420 for i in range(iter_max): +421 +422 y = targetModel.predict(x, batch_size=1, verbose=0) +423 y_c = y[0][label] +424 predictedClass = np.argmax(y, axis=1)[0] +425 +426 if y_c >= y_c_star: +427 if y_c > conf_min and predictedClass == label: +428 # print("Now sampling!") +429 if y_c > globalRandomGen.random(): +430 return x +431 +432 y_c_star = y_c +433 j = 0 +434 else: +435 j = j + 1 +436 if j > rej_max and (k != k_min): +437 k = int(max(k_min, np.ceil(k / 2))) +438 j = 0 +439 +440 x = _randomize_features(x, k) # pyright: ignore +441 +442 # if (i % 20) == 0: +443 # print( +444 # f"{i}/{iter_max}, y_c/y_c*: {y_c:.1%}/{y_c_star:.1%}, pred/class: {predictedClass}/{label}") +445 +446 return None +447 +448 +449def hill_climbing(targetModel: Sequential, numRecords: int, +450 **hyperpars) -> Dataset: +451 """ +452 Generate synthetic data for the shadow models by querying the target model +453 for randomly sampled records, in order to find those that are classified +454 with high confidence. +455 +456 `numRecords`: size of generated dataset +457 `hyperpars` has the following keys (taken from the paper: +458 k_max,k_min,rej_max,conf_min,iter_max) +459 """ +460 +461 # Generate an array of labels, determining which class to synthesize for +462 # TODO: initializing and then changing `features` array might not be most +463 # efficient solution +464 +465 numClasses: int = 100 +466 labels: NDArray = _generate_labels(numClasses, numRecords) +467 +468 numFeatures: int = 600 +469 features: NDArray = np.zeros((numRecords, numFeatures)) +470 +471 for index, label in enumerate(labels): +472 label = int(label[0]) +473 new_record = _generate_synthetic_record(label, targetModel, **hyperpars) +474 while new_record is None: +475 new_record = _generate_synthetic_record(label, targetModel, **hyperpars) +476 print(f"Generating synthetic records: {index}/{numRecords}, {index/numRecords*100:.2f}% done.") +477 features[index] = new_record.reshape((1, numFeatures)) +478 +479 features = features.reshape((numRecords, numFeatures)) +480 labels = labels.reshape((numRecords, 1)) +481 return Dataset.from_tensor_slices((features, labels)) +482 +483def get_target_model_rest_data(config:Dict) -> Dataset: +484 modelName = tm.get_model_name(config) +485 restDataName = modelName + "_rest_data" +486 return ds.load_target(restDataName) +487 +488if __name__ == "__main__": +489 import argparse +490 import configuration as con +491 import datasets as ds +492 import target_models as tm +493 +494 parser = argparse.ArgumentParser(description='Generate all the necessary shadow data and save it to disk.') +495 parser.add_argument('--config', help='Relative path to config file.',) +496 config = con.from_cli_options(vars(parser.parse_args())) +497 set_seed(config["seed"]) +498 +499 targetDataset = get_target_model_rest_data(config) +500 targetModel = tm.load_model(tm.get_model_name(config), verbose=config["verbose"]) +501 get_shadow_data(config, targetDataset, targetModel) @@ -532,16 +603,16 @@

- 
28def set_seed(new_seed: int):
-29    """
-30    Set the global seed that will be used for all functions that include
-31    randomness.
-32    """
-33    global global_seed
-34    global_seed = new_seed
-35    np.random.seed(global_seed)
-36    random.seed(global_seed)
-37    random_seed.set_seed(global_seed)
+            
29def set_seed(new_seed: int):
+30    """
+31    Set the global seed that will be used for all functions that include
+32    randomness.
+33    """
+34    global global_seed
+35    global_seed = new_seed
+36    np.random.seed(global_seed)
+37    random.seed(global_seed)
+38    random_seed.set_seed(global_seed)
 

 
 
@@ -562,14 +633,14 @@
- 
40def generate_shadow_data_sampling(original_data: Dataset) -> Dataset:
-41    """
-42    Generate synthetic data for the shadow models by randomly sampling data
-43    points from the original data set.
-44    """
-45    sample_dataset: Dataset = tf.data.Dataset.sample_from_datasets(
-46        [original_data], seed=global_seed, stop_on_empty_dataset=True)
-47    return sample_dataset
+            
41def generate_shadow_data_sampling(original_data: Dataset) -> Dataset:
+42    """
+43    Generate synthetic data for the shadow models by randomly sampling data
+44    points from the original data set.
+45    """
+46    sample_dataset: Dataset = tf.data.Dataset.sample_from_datasets(
+47        [original_data], seed=global_seed, stop_on_empty_dataset=True)
+48    return sample_dataset
 

 
 
@@ -590,10 +661,10 @@
- 
50def split_shadow_data(config: Dict, shadowData: ds.Dataset) -> List[ds.Dataset]:
-51    print("Splitting shadow data into subsets.")
-52    numSubsets = config["shadowModels"]["number"]
-53    return ds.split_dataset(shadowData, numSubsets)
+            
51def split_shadow_data(config: Dict, shadowData: ds.Dataset) -> List[ds.Dataset]:
+52    print("Splitting shadow data into subsets.")
+53    numSubsets = config["shadowModels"]["number"]
+54    return ds.split_dataset(shadowData, numSubsets)
 

 
 
@@ -611,9 +682,9 @@
- 
56def load_shadow_data(config: Dict):
-57    dataName = get_shadow_data_name(config)
-58    return ds.load_shadow(dataName, verbose=config["verbose"])
+            
57def load_shadow_data(config: Dict):
+58    dataName = get_shadow_data_name(config)
+59    return ds.load_shadow(dataName, verbose=config["verbose"])
 

 
 
@@ -631,33 +702,39 @@
- 
61def get_shadow_data_name(config: Dict):
-62    shadowConfig = config["shadowDataset"]
-63    method = shadowConfig["method"]
-64    targetDataName = config["targetDataset"]["name"]
-65    dataSize = shadowConfig["size"]
-66    hyperpars = shadowConfig[method]["hyperparameters"]
-67    if method == "noisy":
-68        dataName = f'{method}_fraction_{hyperpars["fraction"]}_size_{dataSize}_target_{targetDataName}'
-69    elif method == "hill_climbing":
-70        dataName = \
-71            f'{method}_' + \
-72            f'{targetDataName}_' + \
-73            f'kmax_{hyperpars["k_max"]}_' + \
-74            f'kmin_{hyperpars["k_min"]}_' + \
-75            f'confmin_{hyperpars["conf_min"]}_' + \
-76            f'rejmax_{hyperpars["rej_max"]}_' + \
-77            f'itermax_{hyperpars["iter_max"]}_' + \
-78            f'size_{dataSize}'
-79    elif method == "original":
-80        dataName = \
-81            f'{method}_' + \
-82            f'{targetDataName}_' + \
-83            f'original_data_' + \
-84            f'size_{dataSize}'
-85    else:
-86        raise ValueError(f"{method} is not a valid shadow data method.")
-87    return dataName
+            
62def get_shadow_data_name(config: Dict):
+63    shadowConfig = config["shadowDataset"]
+64    method = shadowConfig["method"]
+65    targetDataName = config["targetDataset"]["name"]
+66    dataSize = shadowConfig["size"]
+67    hyperpars = shadowConfig[method]["hyperparameters"]
+68    if method == "noisy":
+69        dataName = f'{method}_fraction_{hyperpars["fraction"]}_size_{dataSize}_target_{targetDataName}'
+70    elif method == "hill_climbing":
+71        dataName = \
+72            f'{method}_' + \
+73            f'{targetDataName}_' + \
+74            f'kmax_{hyperpars["k_max"]}_' + \
+75            f'kmin_{hyperpars["k_min"]}_' + \
+76            f'confmin_{hyperpars["conf_min"]}_' + \
+77            f'rejmax_{hyperpars["rej_max"]}_' + \
+78            f'itermax_{hyperpars["iter_max"]}_' + \
+79            f'size_{dataSize}'
+80    elif method == "original":
+81        dataName = \
+82            f'{method}_' + \
+83            f'{targetDataName}_' + \
+84            f'original_data_' + \
+85            f'size_{dataSize}'
+86    elif method == "statistic":
+87        dataName = \
+88            f'{method}_' + \
+89            f'{targetDataName}_' + \
+90            f'statistic_' + \
+91            f'size_{dataSize}'
+92    else:
+93        raise ValueError(f"{method} is not a valid shadow data method.")
+94    return dataName
 

 
 
@@ -675,41 +752,43 @@
- 
 90def get_shadow_data(config: Dict, targetDataset, targetModel) -> ds.Dataset:
- 91    verbose = config["verbose"]
- 92    shadowConfig = config["shadowDataset"]
- 93    method = shadowConfig["method"]
- 94    dataSize = shadowConfig["size"]
- 95    hyperpars = shadowConfig[method]["hyperparameters"]
- 96    dataName = get_shadow_data_name(config)
- 97
- 98    try:
- 99        print("Loading shadow data from disk.")
-100        shadowData = load_shadow_data(config)
-101    except BaseException:
-102        print("Loading failed, generating shadow data.")
-103
-104        if method == "noisy":
-105            shadowData = generate_shadow_data_noisy(targetDataset, dataSize, **hyperpars)
-106        elif method == "hill_climbing":
-107            shadowData = hill_climbing(targetModel, dataSize, **hyperpars)
-108        elif method == "original":
-109            modelName = tm.get_model_name(config)
-110            restDataName = modelName + "_rest_data"
-111            shadowData = ds.load_target(restDataName).take(dataSize)
-112        else:
-113            raise ValueError(f"{method} is not a valid shadow data method.")
-114
-115        if verbose:
-116            print(f"Saving shadow data {dataName} to disk.")
-117        try:
-118            ds.save_shadow(shadowData, dataName)
-119        except BaseException:
-120            print(f"Failed to save shadow data {dataName} to disk.")
-121            ds.delete_shadow(dataName)
-122            raise
+            
 97def get_shadow_data(config: Dict, targetDataset, targetModel) -> ds.Dataset:
+ 98    verbose = config["verbose"]
+ 99    shadowConfig = config["shadowDataset"]
+100    method = shadowConfig["method"]
+101    dataSize = shadowConfig["size"]
+102    hyperpars = shadowConfig[method]["hyperparameters"]
+103    dataName = get_shadow_data_name(config)
+104
+105    try:
+106        print("Loading shadow data from disk.")
+107        shadowData = load_shadow_data(config)
+108    except BaseException:
+109        print("Loading failed, generating shadow data.")
+110
+111        if method == "noisy":
+112            shadowData = generate_shadow_data_noisy(targetDataset, dataSize, **hyperpars)
+113        elif method == "hill_climbing":
+114            shadowData = hill_climbing(targetModel, dataSize, **hyperpars)
+115        elif method == "original":
+116            modelName = tm.get_model_name(config)
+117            restDataName = modelName + "_rest_data"
+118            shadowData = ds.load_target(restDataName).take(dataSize)
+119        elif method == "statistic":
+120            shadowData = generate_shadow_data_statistic(config, **hyperpars)
+121        else:
+122            raise ValueError(f"{method} is not a valid shadow data method.")
 123
-124    return shadowData
+124        if verbose:
+125            print(f"Saving shadow data {dataName} to disk.")
+126        try:
+127            ds.save_shadow(shadowData, dataName)
+128        except BaseException:
+129            print(f"Failed to save shadow data {dataName} to disk.")
+130            ds.delete_shadow(dataName)
+131            raise
+132
+133    return shadowData
 

 
 
@@ -727,35 +806,35 @@
- 
145def generate_shadow_data_noisy(original_data: Dataset, outputSize: int, fraction: float = 0.1) -> Dataset:
-146    """
-147    Generate synthetic data for the shadow models by using a noisy version of
-148    the original data.
-149    Returns only the noisy data, no the oririnal data.
-150
-151    Arguments:
-152        fraction: percentage of labels that will be flipped per data record to
-153                  make it "noisy"
-154    """
-155    inputSize = original_data.cardinality().numpy()
-156    # Since outputSize % inputSize not always 0, we have to fill the gap with a subset
-157    # of the full input data. To avoid bias, shuffle the input data.
-158    noisySet = _make_dataset_noisy(ds.shuffle(original_data), fraction)
+            
154def generate_shadow_data_noisy(original_data: Dataset, outputSize: int, fraction: float = 0.1) -> Dataset:
+155    """
+156    Generate synthetic data for the shadow models by using a noisy version of
+157    the original data.
+158    Returns only the noisy data, no the oririnal data.
 159
-160    if inputSize >= outputSize:
-161        return noisySet.take(outputSize)
-162
-163    numNoisyVersions = int(np.floor(outputSize / inputSize))
-164    # How many records to add after collecting numNoisyVersions sets
-165    offset = outputSize % inputSize
-166
-167    for _ in range(numNoisyVersions - 1):
-168        newNoisySet = _make_dataset_noisy(ds.shuffle(original_data), fraction)
-169        noisySet = noisySet.concatenate(newNoisySet)
-170
-171    offsetSet = _make_dataset_noisy(
-172        ds.shuffle(original_data), fraction).take(offset)
-173    return noisySet.concatenate(offsetSet)
+160    Arguments:
+161        fraction: percentage of labels that will be flipped per data record to
+162                  make it "noisy"
+163    """
+164    inputSize = original_data.cardinality().numpy()
+165    # Since outputSize % inputSize not always 0, we have to fill the gap with a subset
+166    # of the full input data. To avoid bias, shuffle the input data.
+167    noisySet = _make_dataset_noisy(ds.shuffle(original_data), fraction)
+168
+169    if inputSize >= outputSize:
+170        return noisySet.take(outputSize)
+171
+172    numNoisyVersions = int(np.floor(outputSize / inputSize))
+173    # How many records to add after collecting numNoisyVersions sets
+174    offset = outputSize % inputSize
+175
+176    for _ in range(numNoisyVersions - 1):
+177        newNoisySet = _make_dataset_noisy(ds.shuffle(original_data), fraction)
+178        noisySet = noisySet.concatenate(newNoisySet)
+179
+180    offsetSet = _make_dataset_noisy(
+181        ds.shuffle(original_data), fraction).take(offset)
+182    return noisySet.concatenate(offsetSet)
 

 
 
@@ -775,18 +854,53 @@ 

 

             
         def
-        generate_shadow_data_statistic(	original_data: tensorflow.python.data.ops.dataset_ops.DatasetV2) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:
+        generate_shadow_data_statistic(	config: Dict,	**hyperpars) -> tensorflow.python.data.ops.dataset_ops.DatasetV2:
 
                 
 
     

     
-            
176def generate_shadow_data_statistic(original_data: Dataset) -> Dataset:
-177    """
-178    Generate synthetic data for the shadow models by using the marginal
-179    distribution of features in the original dataset.
-180    """
-181    pass
+            
210def generate_shadow_data_statistic(config: Dict, **hyperpars) -> Dataset:
+211    """
+212    Generate synthetic data for the shadow models by using the marginal
+213    distribution of features in the original dataset.
+214    """
+215    # TODO: Kaggle specific
+216    size = hyperpars["size"]
+217    try:
+218        marginalProbabilities = ds.load_numpy_array("kaggle_marginals.npy")
+219    except:
+220        marginalProbabilities = _compute_kaggle_marginals(config)
+221        ds.save_numpy_array("kaggle_marginals.npy",marginalProbabilities)
+222
+223    # Generate new records
+224    numClasses = marginalProbabilities.shape[0]
+225    numFeatures = marginalProbabilities.shape[1]
+226
+227    features: NDArray = np.zeros((size,numFeatures)).astype(np.int32)
+228    labels: NDArray = np.zeros((size,numClasses)).astype(np.int32)
+229
+230    recordsPerClass = int(size/numClasses)
+231
+232    for _class in range(numClasses):
+233        print(f"Generating records for class {_class}")
+234        index_start = _class * recordsPerClass
+235        index_end = (_class + 1) * recordsPerClass
+236        #  for index in range(index_start, index_end):
+237        #      labels[index] = to_categorical(_class, num_classes = numClasses)
+238        labels[index_start:index_end] = np.tile(to_categorical(_class, num_classes =
+239                                                               numClasses),recordsPerClass).reshape(recordsPerClass,numClasses)
+240        gen = np.random.default_rng(seed=global_seed)
+241        marginalProbability = marginalProbabilities[_class]
+242
+243        for feature in range(numFeatures):
+244            print(f"\tGenerating records for feature {feature}")
+245            probability = marginalProbability[feature]
+246            # sample one feature for all records in this class at once
+247            sampledFeature = gen.choice([0,1], p=[1-probability,probability],size = recordsPerClass)
+248            features[index_start:index_end,feature] = sampledFeature
+249
+250    return Dataset.from_tensor_slices((features, labels))
 

 
 
@@ -807,39 +921,39 @@ 

 
     

     
-            
379def hill_climbing(targetModel: Sequential, numRecords: int,
-380                  **hyperpars) -> Dataset:
-381    """
-382    Generate synthetic data for the shadow models by querying the target model
-383    for randomly sampled records, in order to find those that are classified
-384    with high confidence.
-385
-386    `numRecords`: size of generated dataset
-387    `hyperpars` has the following keys (taken from the paper:
-388    k_max,k_min,rej_max,conf_min,iter_max)
-389    """
-390
-391    # Generate an array of labels, determining which class to synthesize for
-392    # TODO: initializing and then changing `features` array might not be most
-393    # efficient solution
-394
-395    numClasses: int = 100
-396    labels: NDArray = _generate_labels(numClasses, numRecords)
-397
-398    numFeatures: int = 600
-399    features: NDArray = np.zeros((numRecords, numFeatures))
-400
-401    for index, label in enumerate(labels):
-402        label = int(label[0])
-403        new_record = _generate_synthetic_record(label, targetModel, **hyperpars)
-404        while new_record is None:
-405            new_record = _generate_synthetic_record(label, targetModel, **hyperpars)
-406        print(f"Generating synthetic records: {index}/{numRecords}, {index/numRecords*100:.2f}% done.")
-407        features[index] = new_record.reshape((1, numFeatures))
-408
-409    features = features.reshape((numRecords, numFeatures))
-410    labels = labels.reshape((numRecords, 1))
-411    return Dataset.from_tensor_slices((features, labels))
+            
450def hill_climbing(targetModel: Sequential, numRecords: int,
+451                  **hyperpars) -> Dataset:
+452    """
+453    Generate synthetic data for the shadow models by querying the target model
+454    for randomly sampled records, in order to find those that are classified
+455    with high confidence.
+456
+457    `numRecords`: size of generated dataset
+458    `hyperpars` has the following keys (taken from the paper:
+459    k_max,k_min,rej_max,conf_min,iter_max)
+460    """
+461
+462    # Generate an array of labels, determining which class to synthesize for
+463    # TODO: initializing and then changing `features` array might not be most
+464    # efficient solution
+465
+466    numClasses: int = 100
+467    labels: NDArray = _generate_labels(numClasses, numRecords)
+468
+469    numFeatures: int = 600
+470    features: NDArray = np.zeros((numRecords, numFeatures))
+471
+472    for index, label in enumerate(labels):
+473        label = int(label[0])
+474        new_record = _generate_synthetic_record(label, targetModel, **hyperpars)
+475        while new_record is None:
+476            new_record = _generate_synthetic_record(label, targetModel, **hyperpars)
+477        print(f"Generating synthetic records: {index}/{numRecords}, {index/numRecords*100:.2f}% done.")
+478        features[index] = new_record.reshape((1, numFeatures))
+479
+480    features = features.reshape((numRecords, numFeatures))
+481    labels = labels.reshape((numRecords, 1))
+482    return Dataset.from_tensor_slices((features, labels))
 

 
 
@@ -865,10 +979,10 @@ 

 
     

     
-            
413def get_target_model_rest_data(config:Dict) -> Dataset:
-414    modelName = tm.get_model_name(config)
-415    restDataName = modelName + "_rest_data"
-416    return ds.load_target(restDataName)
+            
484def get_target_model_rest_data(config:Dict) -> Dataset:
+485    modelName = tm.get_model_name(config)
+486    restDataName = modelName + "_rest_data"
+487    return ds.load_target(restDataName)
 

 
 
diff --git a/shadow_models.html b/shadow_models.html
index 8c18497..bc82e85 100644
--- a/shadow_models.html
+++ b/shadow_models.html
@@ -93,111 +93,112 @@ 

  23
  24def get_shadow_model_name(config: Dict, i: int):
  25    numModels: int = config["shadowModels"]["number"]
- 26    split: float = config["shadowModels"]["split"]
- 27    return "shadow_" + tm.get_model_name(config) + f"_split_{split}_{i+1}_of_{numModels}"
- 28
+ 26    method: str = config["shadowDataset"]["method"]
+ 27    split: float = config["shadowModels"]["split"]
+ 28    return "shadow_" + method + tm.get_model_name(config) + f"_split_{split}_{i+1}_of_{numModels}"
  29
- 30def load_shadow_models_and_datasets(config: Dict) -> Tuple[List[tm.Sequential], List[Tuple[ds.Dataset, ds.Dataset]]]:
- 31    verbose = config["verbose"]
- 32    numModels: int = config["shadowModels"]["number"]
- 33    datasets = []
- 34    models = []
- 35
- 36    print(f"Loading shadow models from disk.")
- 37    for i in range(numModels):
- 38        modelName = get_shadow_model_name(config, i)
- 39        model: tm.KaggleModel = tm.load_model(modelName, verbose=verbose)
- 40
- 41        trainDataName = modelName + "_train_data"
- 42        testDataName = modelName + "_test_data"
- 43        trainData: ds.Dataset = ds.load_shadow(trainDataName, verbose=verbose)
- 44        testData: ds.Dataset = ds.load_shadow(testDataName, verbose=verbose)
- 45
- 46        datasets.append((trainData, testData))
- 47        models.append(model)
- 48
- 49    return models, datasets
- 50
+ 30
+ 31def load_shadow_models_and_datasets(config: Dict) -> Tuple[List[tm.Sequential], List[Tuple[ds.Dataset, ds.Dataset]]]:
+ 32    verbose = config["verbose"]
+ 33    numModels: int = config["shadowModels"]["number"]
+ 34    datasets = []
+ 35    models = []
+ 36
+ 37    print(f"Loading shadow models from disk.")
+ 38    for i in range(numModels):
+ 39        modelName = get_shadow_model_name(config, i)
+ 40        model: tm.KaggleModel = tm.load_model(modelName, verbose=verbose)
+ 41
+ 42        trainDataName = modelName + "_train_data"
+ 43        testDataName = modelName + "_test_data"
+ 44        trainData: ds.Dataset = ds.load_shadow(trainDataName, verbose=verbose)
+ 45        testData: ds.Dataset = ds.load_shadow(testDataName, verbose=verbose)
+ 46
+ 47        datasets.append((trainData, testData))
+ 48        models.append(model)
+ 49
+ 50    return models, datasets
  51
- 52def train_shadow_models(config: Dict, shadowDatasets: List[ds.Dataset]
- 53                        ) -> Tuple[List[tm.Sequential], List[Tuple[ds.Dataset, ds.Dataset]]]:
- 54
- 55    numModels: int = config["shadowModels"]["number"]
- 56    split: float = config["shadowModels"]["split"]
- 57    dataSize = shadowDatasets[0].cardinality().numpy()
- 58    assert dataSize != 0, "Loaded shadow dataset that seems empty."
- 59    trainSize = np.ceil(split * dataSize)
- 60    testSize = dataSize - trainSize
- 61    datasets = []
- 62    models = []
- 63
- 64    for i in range(numModels):
- 65        print(f"Training shadow model {i+1}.")
- 66
- 67        modelName = get_shadow_model_name(config, i)
- 68        trainDataName = modelName + "_train_data"
- 69        testDataName = modelName + "_test_data"
- 70
- 71        dataset = shadowDatasets[i]
- 72        trainData = dataset.take(trainSize)
- 73        testData = dataset.skip(trainSize).take(testSize)
- 74
- 75        # Shadow models have same architecture as target model
- 76        model = tm.KaggleModel(config["targetModel"]["classes"])
- 77        modelConfig = config["targetModel"]["hyperparameters"]
- 78
- 79        tm.train_model(model, modelName, trainData, testData, modelConfig)
- 80
- 81        print(f"Saving shadow model {i+1} and its data to disk.")
- 82        tm.save_model(modelName, model)
- 83        ds.save_shadow(trainData, trainDataName)
- 84        ds.save_shadow(testData, testDataName)
- 85
- 86        print(f"Evaluating shadow model {i+1}")
- 87        tm.evaluate_model(model, testData)
- 88
- 89        datasets.append((trainData, testData))
- 90        models.append(model)
- 91
- 92    return models, datasets
- 93
+ 52
+ 53def train_shadow_models(config: Dict, shadowDatasets: List[ds.Dataset]
+ 54                        ) -> Tuple[List[tm.Sequential], List[Tuple[ds.Dataset, ds.Dataset]]]:
+ 55
+ 56    numModels: int = config["shadowModels"]["number"]
+ 57    split: float = config["shadowModels"]["split"]
+ 58    dataSize = shadowDatasets[0].cardinality().numpy()
+ 59    assert dataSize != 0, "Loaded shadow dataset that seems empty."
+ 60    trainSize = np.ceil(split * dataSize)
+ 61    testSize = dataSize - trainSize
+ 62    datasets = []
+ 63    models = []
+ 64
+ 65    for i in range(numModels):
+ 66        print(f"Training shadow model {i+1}.")
+ 67
+ 68        modelName = get_shadow_model_name(config, i)
+ 69        trainDataName = modelName + "_train_data"
+ 70        testDataName = modelName + "_test_data"
+ 71
+ 72        dataset = shadowDatasets[i]
+ 73        trainData = dataset.take(trainSize)
+ 74        testData = dataset.skip(trainSize).take(testSize)
+ 75
+ 76        # Shadow models have same architecture as target model
+ 77        model = tm.KaggleModel(config["targetModel"]["classes"])
+ 78        modelConfig = config["targetModel"]["hyperparameters"]
+ 79
+ 80        tm.train_model(model, modelName, trainData, testData, modelConfig)
+ 81
+ 82        print(f"Saving shadow model {i+1} and its data to disk.")
+ 83        tm.save_model(modelName, model)
+ 84        ds.save_shadow(trainData, trainDataName)
+ 85        ds.save_shadow(testData, testDataName)
+ 86
+ 87        print(f"Evaluating shadow model {i+1}")
+ 88        tm.evaluate_model(model, testData)
+ 89
+ 90        datasets.append((trainData, testData))
+ 91        models.append(model)
+ 92
+ 93    return models, datasets
  94
- 95def get_shadow_models_and_datasets(config: Dict, shadowDatasets: List[ds.Dataset]
- 96                                   ) -> Tuple[List[tm.Sequential], List[Tuple[ds.Dataset, ds.Dataset]]]:
- 97    """
- 98    Tries to load shadow datasets from disk, alternatively trains from scratch.
- 99
-100    Returns 2 lists:
-101        models: the trained shadow models and a list of tuples, containing
-102        datasets: the training and test data for the corresponding shadow models
-103
-104        E.g. models[0] is trained with datasets[0,0] and tested on datasets[0,1]
-105    """
-106    try:
-107        print("Trying to load shadow models and data from disk.")
-108        models, datasets = load_shadow_models_and_datasets(config)
-109    except BaseException:
-110        print("Didn't work, training shadow models.")
-111        models, datasets = train_shadow_models(config, shadowDatasets)
-112
-113    return models, datasets
-114
+ 95
+ 96def get_shadow_models_and_datasets(config: Dict, shadowDatasets: List[ds.Dataset]
+ 97                                   ) -> Tuple[List[tm.Sequential], List[Tuple[ds.Dataset, ds.Dataset]]]:
+ 98    """
+ 99    Tries to load shadow datasets from disk, alternatively trains from scratch.
+100
+101    Returns 2 lists:
+102        models: the trained shadow models and a list of tuples, containing
+103        datasets: the training and test data for the corresponding shadow models
+104
+105        E.g. models[0] is trained with datasets[0,0] and tested on datasets[0,1]
+106    """
+107    try:
+108        print("Trying to load shadow models and data from disk.")
+109        models, datasets = load_shadow_models_and_datasets(config)
+110    except BaseException:
+111        print("Didn't work, training shadow models.")
+112        models, datasets = train_shadow_models(config, shadowDatasets)
+113
+114    return models, datasets
 115
-116if __name__ == "__main__":
-117    import argparse
-118    import configuration as con
-119    import datasets as ds
-120    import target_models as tm
-121    import shadow_data as sd
-122
-123    parser = argparse.ArgumentParser(description='Save one shadow dataset per model and train the models.')
-124    parser.add_argument('--config', help='Relative path to config file.',)
-125    config = con.from_cli_options(vars(parser.parse_args()))
-126    set_seed(config["seed"])
-127
-128    shadowData = sd.load_shadow_data(config)
-129    shadowDatasets = sd.split_shadow_data(config, shadowData)
-130    shadowModels = get_shadow_models_and_datasets(config, shadowDatasets)
+116
+117if __name__ == "__main__":
+118    import argparse
+119    import configuration as con
+120    import datasets as ds
+121    import target_models as tm
+122    import shadow_data as sd
+123
+124    parser = argparse.ArgumentParser(description='Save one shadow dataset per model and train the models.')
+125    parser.add_argument('--config', help='Relative path to config file.',)
+126    config = con.from_cli_options(vars(parser.parse_args()))
+127    set_seed(config["seed"])
+128
+129    shadowData = sd.load_shadow_data(config)
+130    shadowDatasets = sd.split_shadow_data(config, shadowData)
+131    shadowModels = get_shadow_models_and_datasets(config, shadowDatasets)
 

 
 
@@ -243,8 +244,9 @@ 

     
             
25def get_shadow_model_name(config: Dict, i: int):
 26    numModels: int = config["shadowModels"]["number"]
-27    split: float = config["shadowModels"]["split"]
-28    return "shadow_" + tm.get_model_name(config) + f"_split_{split}_{i+1}_of_{numModels}"
+27    method: str = config["shadowDataset"]["method"]
+28    split: float = config["shadowModels"]["split"]
+29    return "shadow_" + method + tm.get_model_name(config) + f"_split_{split}_{i+1}_of_{numModels}"
 

 
 
@@ -262,26 +264,26 @@
- 
31def load_shadow_models_and_datasets(config: Dict) -> Tuple[List[tm.Sequential], List[Tuple[ds.Dataset, ds.Dataset]]]:
-32    verbose = config["verbose"]
-33    numModels: int = config["shadowModels"]["number"]
-34    datasets = []
-35    models = []
-36
-37    print(f"Loading shadow models from disk.")
-38    for i in range(numModels):
-39        modelName = get_shadow_model_name(config, i)
-40        model: tm.KaggleModel = tm.load_model(modelName, verbose=verbose)
-41
-42        trainDataName = modelName + "_train_data"
-43        testDataName = modelName + "_test_data"
-44        trainData: ds.Dataset = ds.load_shadow(trainDataName, verbose=verbose)
-45        testData: ds.Dataset = ds.load_shadow(testDataName, verbose=verbose)
-46
-47        datasets.append((trainData, testData))
-48        models.append(model)
-49
-50    return models, datasets
+            
32def load_shadow_models_and_datasets(config: Dict) -> Tuple[List[tm.Sequential], List[Tuple[ds.Dataset, ds.Dataset]]]:
+33    verbose = config["verbose"]
+34    numModels: int = config["shadowModels"]["number"]
+35    datasets = []
+36    models = []
+37
+38    print(f"Loading shadow models from disk.")
+39    for i in range(numModels):
+40        modelName = get_shadow_model_name(config, i)
+41        model: tm.KaggleModel = tm.load_model(modelName, verbose=verbose)
+42
+43        trainDataName = modelName + "_train_data"
+44        testDataName = modelName + "_test_data"
+45        trainData: ds.Dataset = ds.load_shadow(trainDataName, verbose=verbose)
+46        testData: ds.Dataset = ds.load_shadow(testDataName, verbose=verbose)
+47
+48        datasets.append((trainData, testData))
+49        models.append(model)
+50
+51    return models, datasets
 

 
 
@@ -299,47 +301,47 @@
- 
53def train_shadow_models(config: Dict, shadowDatasets: List[ds.Dataset]
-54                        ) -> Tuple[List[tm.Sequential], List[Tuple[ds.Dataset, ds.Dataset]]]:
-55
-56    numModels: int = config["shadowModels"]["number"]
-57    split: float = config["shadowModels"]["split"]
-58    dataSize = shadowDatasets[0].cardinality().numpy()
-59    assert dataSize != 0, "Loaded shadow dataset that seems empty."
-60    trainSize = np.ceil(split * dataSize)
-61    testSize = dataSize - trainSize
-62    datasets = []
-63    models = []
-64
-65    for i in range(numModels):
-66        print(f"Training shadow model {i+1}.")
-67
-68        modelName = get_shadow_model_name(config, i)
-69        trainDataName = modelName + "_train_data"
-70        testDataName = modelName + "_test_data"
-71
-72        dataset = shadowDatasets[i]
-73        trainData = dataset.take(trainSize)
-74        testData = dataset.skip(trainSize).take(testSize)
-75
-76        # Shadow models have same architecture as target model
-77        model = tm.KaggleModel(config["targetModel"]["classes"])
-78        modelConfig = config["targetModel"]["hyperparameters"]
-79
-80        tm.train_model(model, modelName, trainData, testData, modelConfig)
-81
-82        print(f"Saving shadow model {i+1} and its data to disk.")
-83        tm.save_model(modelName, model)
-84        ds.save_shadow(trainData, trainDataName)
-85        ds.save_shadow(testData, testDataName)
-86
-87        print(f"Evaluating shadow model {i+1}")
-88        tm.evaluate_model(model, testData)
-89
-90        datasets.append((trainData, testData))
-91        models.append(model)
-92
-93    return models, datasets
+            
54def train_shadow_models(config: Dict, shadowDatasets: List[ds.Dataset]
+55                        ) -> Tuple[List[tm.Sequential], List[Tuple[ds.Dataset, ds.Dataset]]]:
+56
+57    numModels: int = config["shadowModels"]["number"]
+58    split: float = config["shadowModels"]["split"]
+59    dataSize = shadowDatasets[0].cardinality().numpy()
+60    assert dataSize != 0, "Loaded shadow dataset that seems empty."
+61    trainSize = np.ceil(split * dataSize)
+62    testSize = dataSize - trainSize
+63    datasets = []
+64    models = []
+65
+66    for i in range(numModels):
+67        print(f"Training shadow model {i+1}.")
+68
+69        modelName = get_shadow_model_name(config, i)
+70        trainDataName = modelName + "_train_data"
+71        testDataName = modelName + "_test_data"
+72
+73        dataset = shadowDatasets[i]
+74        trainData = dataset.take(trainSize)
+75        testData = dataset.skip(trainSize).take(testSize)
+76
+77        # Shadow models have same architecture as target model
+78        model = tm.KaggleModel(config["targetModel"]["classes"])
+79        modelConfig = config["targetModel"]["hyperparameters"]
+80
+81        tm.train_model(model, modelName, trainData, testData, modelConfig)
+82
+83        print(f"Saving shadow model {i+1} and its data to disk.")
+84        tm.save_model(modelName, model)
+85        ds.save_shadow(trainData, trainDataName)
+86        ds.save_shadow(testData, testDataName)
+87
+88        print(f"Evaluating shadow model {i+1}")
+89        tm.evaluate_model(model, testData)
+90
+91        datasets.append((trainData, testData))
+92        models.append(model)
+93
+94    return models, datasets
 

 
 
@@ -357,25 +359,25 @@
- 
 96def get_shadow_models_and_datasets(config: Dict, shadowDatasets: List[ds.Dataset]
- 97                                   ) -> Tuple[List[tm.Sequential], List[Tuple[ds.Dataset, ds.Dataset]]]:
- 98    """
- 99    Tries to load shadow datasets from disk, alternatively trains from scratch.
-100
-101    Returns 2 lists:
-102        models: the trained shadow models and a list of tuples, containing
-103        datasets: the training and test data for the corresponding shadow models
-104
-105        E.g. models[0] is trained with datasets[0,0] and tested on datasets[0,1]
-106    """
-107    try:
-108        print("Trying to load shadow models and data from disk.")
-109        models, datasets = load_shadow_models_and_datasets(config)
-110    except BaseException:
-111        print("Didn't work, training shadow models.")
-112        models, datasets = train_shadow_models(config, shadowDatasets)
-113
-114    return models, datasets
+            
 97def get_shadow_models_and_datasets(config: Dict, shadowDatasets: List[ds.Dataset]
+ 98                                   ) -> Tuple[List[tm.Sequential], List[Tuple[ds.Dataset, ds.Dataset]]]:
+ 99    """
+100    Tries to load shadow datasets from disk, alternatively trains from scratch.
+101
+102    Returns 2 lists:
+103        models: the trained shadow models and a list of tuples, containing
+104        datasets: the training and test data for the corresponding shadow models
+105
+106        E.g. models[0] is trained with datasets[0,0] and tested on datasets[0,1]
+107    """
+108    try:
+109        print("Trying to load shadow models and data from disk.")
+110        models, datasets = load_shadow_models_and_datasets(config)
+111    except BaseException:
+112        print("Didn't work, training shadow models.")
+113        models, datasets = train_shadow_models(config, shadowDatasets)
+114
+115    return models, datasets