Sign-Verify.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying Convolutional Neural Networks to Cryptography for Digital Signing/Verification (Exploratory Points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By Ria Cheruvu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To tackle the issue of secure communication, I employed Convolutional Neural Networks (CNNs) for the encryption/decryption (128-bit key) and signing/verification functions. \n",
    "\n",
    "The following ipynb contains the parameter Initialization,variable declaration, weight definition, model definition, user input, training, testing, and performance benchmarking against Fernet for signing/verification functions. \n",
    "\n",
    "The code is self-explanatory. To run the code, please run the cells in the TensorFlow framework one at a time.\n",
    "\n",
    "Please Note: User input is mandatory to achieve results. \n",
    "\n",
    "I ran the following code through Docker on a Windows 10 Machine with 16 GB RAM and 8-core 3.1 GHz CPU with hyper-threading."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The digital signature model is similar to that used for encryption and decryption. It requires key generation, a signing algorithm, and a signature-verifying algorithm. \n",
    "The process for signing and verifying is as follows: Alice sends plaintext and her signature to Bob, who takes Alice’s plaintext, computes the signature and compares it to the signature received. If the signature Bob computed and the signature Alice sent along with her plain text message are the same, then the message’s integrity can be guaranteed; if not, then Bob will know that Eve or another third party has modified the original message. \n",
    "\n",
    "Till date, a neural cryptography algorithm that demonstrates a signature/verification capability could not be found.  This notebook presents a successful implementation of digital signatures that uses the internal hidden layer states of Bob’s and Alice’s CNN machines to represent the 32-byte signatures.\n",
    "\n",
    "Due to the above reason, I believe this work deserves exploratory points.\n",
    "\n",
    "Further improvement is required to strengthen the algorithm. The code for this algorithm is located in a separate .ipynb file. I wanted to see how the length of the input PlainText would influence verification of the signatures, so four strings of different length were entered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import socket\n",
    "import sys\n",
    "import pickle\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter Initialization "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hyperparameters we are using to tune/experiment with the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "crypto_msg_len = N = 16\n",
    "crypto_key_len = 16\n",
    "batch_size = 512\n",
    "epochs = 1 #50\n",
    "learning_rate = 0.0008 #Optimizer learning rate\n",
    "\n",
    "# Function to generate n random messages and keys\n",
    "def gen_data(n=batch_size, msg_len=crypto_msg_len, key_len=crypto_key_len):\n",
    "    #return (np.random.randint(0, 2, size=(n, msg_len))*2-1), (np.random.randint(0, 2, size=(n, key_len))*2-1)\n",
    "    return (np.random.randint(0, 2, size=(n, msg_len))), (np.random.randint(0, 2, size=(n, key_len)))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def conv1d(input_, filter_shape, stride, name=\"conv1d\"):\n",
    "    with tf.variable_scope(name):\n",
    "        w = tf.get_variable('w', shape=filter_shape, initializer=tf.contrib.layers.xavier_initializer())\n",
    "        conv = tf.nn.conv1d(input_, w, stride, padding='SAME')\n",
    "        return conv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Cryptography Model definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create the original algorithm, I modified adopted code from Liam Schoneveld and GitHub user ankeshanand  to include the convolutional layers and experimented with the parameters by changing the learning rate of the optimizer, the epochs for training and testing, the initial weights, etc. I used the TensorFlow framework and abandoned implementation with the Theano framework, due to lack of CPU optimization libraries on Windows 10; NCA has been implemented in Theano, TensorFlow, and regular python framework by different authors, however, the base algorithm remains the same. I ran the following code through Docker on a Windows 10 Machine with 16 GB RAM and 8-core 3.1 GHz CPU with hyper-threading.\n",
    "\n",
    "I experimented with the Adam optimizer, Adagrad optimizer, and Momentum optimizer, which use the loss functions for minimizing error. Next, I designed the UI accompanying the NCA algorithm. The user can enter input in ASCII, which is then converted to binary and structured appropriately in order to be accepted by np.array for both encryption/decryption and signing/verification functions.\n",
    "\n",
    "The section for socket communication is included for future purposes and can be ignored. For socket communication, the user (Alice) types in a plaintext message, which is then encrypted (cipher text) and sent to Bob’s socket. Upon receiving the cipher text, Bob’s shared key is used to decrypt the message and Bob prints out the recovered message. \n",
    "\n",
    "### Adopted from <cite>\"Learning to Protect Communications with Adversarial Neural Cryptography\" by Abadi, M & Andersen, D.</cite><br/><cite>https://nlml.github.io/neural-networks/adversarial-neural-cryptography/</cite><br/><cite> https://github.com/ankeshanand/neural-cryptography-tensorflow</cite> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Placeholder variables for Message and Key\n",
    "msg = tf.placeholder(\"float\", [None, crypto_msg_len])\n",
    "key = tf.placeholder(\"float\", [None, crypto_key_len])\n",
    "\n",
    "# Weights for fully connected layers\n",
    "w_alice = tf.get_variable(\"alice_w\", shape=[2 * N, 2 * N], initializer=tf.contrib.layers.xavier_initializer())\n",
    "w_bob = tf.get_variable(\"bob_w\", shape=[2 * N, 2 * N], initializer=tf.contrib.layers.xavier_initializer())\n",
    "w_eve1 = tf.get_variable(\"eve_w1\", shape=[N, 2 * N], initializer=tf.contrib.layers.xavier_initializer())\n",
    "w_eve2 = tf.get_variable(\"eve_w2\", shape=[2 * N, 2 * N], initializer=tf.contrib.layers.xavier_initializer())\n",
    "\n",
    "# Alice's Machine Network\n",
    "# FC layer -> Conv Layer (4 1-D convolutions)\n",
    "alice_input = tf.concat(concat_dim=1, values=[msg, key])\n",
    "alice_hidden = tf.nn.sigmoid(tf.matmul(alice_input, w_alice))\n",
    "alice_hidden = tf.expand_dims(alice_hidden, 2)\n",
    "\n",
    "h0 = tf.nn.relu(conv1d(alice_hidden, [4,1,2], stride=1, name=\"alice\"+'_h0_conv'))\n",
    "h1 = tf.nn.relu(conv1d(h0, [2,2,4], stride=2, name=\"alice\"+'_h1_conv'))\n",
    "h2_alice = tf.nn.relu(conv1d(h1, [1,4,4], stride=1, name=\"alice\"+'_h2_conv'))\n",
    "h3 = tf.nn.tanh(conv1d(h2_alice, [1,4,1], stride=1, name=\"alice\"+'_h3_conv'))\n",
    "alice_output = tf.squeeze(h3) # eliminate dimensions of size 1 from the shape of a tensor\n",
    "\n",
    "# Bob's Machine Network (gets the output (cipher text) of Alice's network)\n",
    "# FC layer -> Conv Layer (4 1-D convolutions)\n",
    "bob_input = tf.concat(concat_dim=1, values=[msg, key])\n",
    "bob_hidden = tf.nn.sigmoid(tf.matmul(bob_input, w_bob))\n",
    "bob_hidden = tf.expand_dims(bob_hidden, 2)\n",
    "\n",
    "h0 = tf.nn.relu(conv1d(bob_hidden, [4,1,2], stride=1, name=\"bob\"+'_h0_conv'))\n",
    "h1 = tf.nn.relu(conv1d(h0, [2,2,4], stride=2, name=\"bob\"+'_h1_conv'))\n",
    "h2_bob = tf.nn.relu(conv1d(h1, [1,4,4], stride=1, name=\"bob\"+'_h2_conv'))\n",
    "h3 = tf.nn.tanh(conv1d(h2_bob, [1,4,1], stride=1, name=\"bob\"+'_h3_conv'))\n",
    "bob_output = tf.squeeze(h3) # eliminate dimensions of size 1 from the shape of a tensor\n",
    "\n",
    "# Eve's Machine Network\n",
    "# FC layer -> FC layer -> Conv Layer (4 1-D convolutions)\n",
    "eve_input = alice_output\n",
    "eve_hidden1 = tf.nn.sigmoid(tf.matmul(eve_input, w_eve1))\n",
    "eve_hidden2 = tf.nn.sigmoid(tf.matmul(eve_hidden1, w_eve2))\n",
    "eve_hidden2 = tf.expand_dims(eve_hidden2, 2)\n",
    "\n",
    "h0 = tf.nn.relu(conv1d(eve_hidden2, [4,1,2], stride=1, name=\"eve\"+'_h0_conv'))\n",
    "h1 = tf.nn.relu(conv1d(h0, [2,2,4], stride=2, name=\"eve\"+'_h1_conv'))\n",
    "h2 = tf.nn.relu(conv1d(h1, [1,4,4], stride=1, name=\"eve\"+'_h2_conv'))\n",
    "h3 = tf.nn.tanh(conv1d(h2, [1,4,1], stride=1, name=\"eve\"+'_h3_conv'))\n",
    "eve_output = tf.squeeze(h3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Cryptography Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "alice_errors, bob_errors, eve_errors = [], [], []\n",
    "\n",
    "# Loss Functions\n",
    "decrypt_err_eve = tf.reduce_mean(tf.abs(msg - eve_output))\n",
    "decrypt_err_alice = tf.reduce_mean(tf.abs(msg - alice_output))\n",
    "loss_alice = decrypt_err_alice + (1. - decrypt_err_eve) ** 2.\n",
    "decrypt_err_bob = tf.reduce_mean(tf.abs(msg - bob_output))\n",
    "loss_bob = decrypt_err_bob + (1. - decrypt_err_eve) ** 2.\n",
    "\n",
    "# Get training variables corresponding to each network\n",
    "t_vars = tf.trainable_variables()\n",
    "alice_vars = [var for var in t_vars if 'alice_' in var.name]\n",
    "bob_vars =   [var for var in t_vars if 'bob_' in var.name]\n",
    "eve_vars =   [var for var in t_vars if 'eve_' in var.name]\n",
    "\n",
    "# Build the optimizers, can play with different optimizers\n",
    "\n",
    "'''\n",
    "alice_optimizer = tf.train.AdagradOptimizer(learning_rate, initial_accumulator_value=0.1, \n",
    "                       use_locking=False, name='Adagrad').minimize(loss_alice, var_list=alice_vars)\n",
    "bob_optimizer = tf.train.AdagradOptimizer(learning_rate, initial_accumulator_value=0.1, \n",
    "                        use_locking=False, name='Adagrad').minimize(loss_bob, var_list=bob_vars)\n",
    "eve_optimizer = tf.train.AdagradOptimizer(learning_rate, initial_accumulator_value=0.1, \n",
    "                        use_locking=False, name='Adagrad').minimize(decrypt_err_eve, var_list=eve_vars)\n",
    "\n",
    "alice_optimizer = tf.train.MomentumOptimizer(0.01, 0.9).minimize(loss_alice, var_list=alice_vars)   \n",
    "bob_optimizer = tf.train.MomentumOptimizer(0.01, 0.9).minimize(loss_bob, var_list=bob_vars)\n",
    "eve_optimizer = tf.train.MomentumOptimizer(0.01, 0.9).minimize(decrypt_err_eve, var_list=eve_vars)\n",
    "\n",
    "'''\n",
    "alice_optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss_alice, var_list=alice_vars)   \n",
    "bob_optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss_bob, var_list=bob_vars)\n",
    "eve_optimizer = tf.train.AdamOptimizer(learning_rate).minimize(decrypt_err_eve, var_list=eve_vars)\n",
    "\n",
    "def train(sess):\n",
    "    # Begin Training\n",
    "    tf.initialize_all_variables().run(session=sess)\n",
    "    for i in range(epochs):\n",
    "        iterations = 2000\n",
    "\n",
    "        alice_loss, _, _ = _train('alice', iterations, sess)\n",
    "        alice_errors.append(alice_loss)\n",
    "        print( 'Training Alice, Epoch:', i + 1, ' error: ', alice_loss)\n",
    "\n",
    "        _, bob_loss, _ = _train('bob', iterations, sess)\n",
    "        bob_errors.append(bob_loss)\n",
    "        print( 'Training Bob, Epoch:', i + 1, ' error: ', bob_loss)\n",
    "\n",
    "        _, _, eve_loss = _train('eve', iterations, sess)\n",
    "        eve_errors.append(eve_loss)   \n",
    "        print( 'Training Eve, Epoch:', i + 1, ' error: ', eve_loss)\n",
    "\n",
    "\n",
    "def _train(network, iterations, sess):\n",
    "    alice_decrypt_error, bob_decrypt_error, eve_decrypt_error = 1., 1., 1.\n",
    "\n",
    "    bs = batch_size\n",
    "    # Train Eve for two minibatches to give it a slight computational edge\n",
    "    if network == 'eve':\n",
    "        bs *= 2\n",
    "\n",
    "    for i in range(iterations):\n",
    "        msg_in_val, key_val = gen_data(n=bs, msg_len=crypto_msg_len, key_len=crypto_key_len)\n",
    "        feed_dict={msg: msg_in_val, key: key_val}\n",
    "        if network == 'alice':\n",
    "            _, decrypt_err = sess.run([alice_optimizer, decrypt_err_alice], feed_dict = feed_dict)\n",
    "            alice_decrypt_error = min(alice_decrypt_error, decrypt_err)\n",
    "        elif network == 'bob':\n",
    "            _, decrypt_err = sess.run([bob_optimizer, decrypt_err_bob], feed_dict = feed_dict)\n",
    "            bob_decrypt_error = min(bob_decrypt_error, decrypt_err)\n",
    "        elif network == 'eve':\n",
    "            _, decrypt_err = sess.run([eve_optimizer, decrypt_err_eve], feed_dict = feed_dict)\n",
    "            eve_decrypt_error = min(eve_decrypt_error, decrypt_err)\n",
    "\n",
    "    return alice_decrypt_error, bob_decrypt_error, eve_decrypt_error\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Cryptography Testing Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section of the code takes input and prepares it for the socket communication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter some text: Technology Transcends Humanity and Artificial Intelligence is coming!\n",
      "01010100 01100101 01100011 01101000 01101110 01101111 01101100 01101111 01100111 01111001 00100000 01010100 01110010 01100001 01101110 01110011 01100011 01100101 01101110 01100100 01110011 00100000 01001000 01110101 01101101 01100001 01101110 01101001 01110100 01111001 00100000 01100001 01101110 01100100 00100000 01000001 01110010 01110100 01101001 01100110 01101001 01100011 01101001 01100001 01101100 00100000 01001001 01101110 01110100 01100101 01101100 01101100 01101001 01100111 01100101 01101110 01100011 01100101 00100000 01101001 01110011 00100000 01100011 01101111 01101101 01101001 01101110 01100111 00100001\n",
      "plaintext_to_Alice =  [[ 0.  1.  0.  1.  0.  1.  0.  0.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  1.  0.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  1.  1.  1.  0.  1.  1.  1.  1.  0.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  0.  1.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  0.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  1.  0.  0.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  1.  0.  0.  0.  0.  1.  1.  1.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  1.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  1.  0.  0.  0.  1.  1.  1.  1.  0.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  0.  0.  1.  1.  1.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  1.  1.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  0.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  1.  0.  0.  1.  0.  1.  1.  0.  1.  1.  1.  0.]\n",
      " [ 0.  1.  1.  1.  0.  1.  0.  0.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  1.  0.  1.  0.  1.  1.  0.  1.  1.  1.  0.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  1.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  1.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "text = input('Enter some text: ')\n",
    "bintext = ' '.join('{0:08b}'.format(ord(x), 'b') for x in text)\n",
    "print (bintext)\n",
    "b1  = bintext.replace(\" \", \"\")\n",
    "#b1 = b1.zfill(48)\n",
    "pad = len(b1)%16\n",
    "\n",
    "v1 = np.array([])\n",
    "for i in range(0, len(b1)):\n",
    "    v1 = np.append(v1, int(b1[i]))\n",
    "\n",
    "#apply the padding\n",
    "for i in range(0, pad):\n",
    "    v1 = np.append(v1, int(0))\n",
    "total_len = len(b1) + pad\n",
    "\n",
    "plaintext_to_Alice = v1.reshape(int(total_len/16), 16)\n",
    "print('plaintext_to_Alice = ', plaintext_to_Alice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Socket Communication (Ignore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are sending the message as Alice to Bob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sock_send(message):\n",
    "    # Create a TCP/IP socket\n",
    "    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
    "    server_address = ('localhost', 10001)\n",
    "    sock.connect(server_address)\n",
    "    #To send data over network, you need to serialize it into an array of bytes,\n",
    "    #then deserialize it back. In Python, serialization of most objects can be done via pickle module:\n",
    "    print('message = ', message)\n",
    "    msg2 = pickle.dumps(message)\n",
    "    #print('msg2 = ', msg2)\n",
    "    sock.send(msg2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting up on localhost port 10001\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def sock_recv_init():\n",
    "    # Create a TCP/IP socket\n",
    "    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
    "    # Bind the socket to the port\n",
    "    address = ('localhost', 10001)\n",
    "    print('starting up on %s port %s' % address)\n",
    "    sock.bind(address)\n",
    "    # Listen for incoming connections\n",
    "    sock.listen(1)\n",
    "    return sock\n",
    "\n",
    "def sock_recv(sock):\n",
    "    # Wait for a connection\n",
    "    print( 'waiting for a connection')\n",
    "    connection, client_address = sock.accept()\n",
    "    print('connection from', client_address)\n",
    "    # Receive the data (max 48 bytes)\n",
    "    data = connection.recv(1024*10)\n",
    "    #print('received \"%s\"' % data)\n",
    "    data1 = pickle.loads(data)\n",
    "    print('received ', data1)\n",
    "    return data1\n",
    "\n",
    "sock = sock_recv_init()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "test_file_msg = \"testmsg.txt\"\n",
    "test_file_keys = \"testkey.txt\"\n",
    "alice_output_sign = 0\n",
    "\n",
    "def test(network, sess):\n",
    "        alice_decrypt_error, bob_decrypt_error, eve_decrypt_error = 1., 1., 1.\n",
    "        alice_encrypt_time = 0\n",
    "        bob_decrypt_time = 0\n",
    "        bob_output_1 = 0\n",
    "        alice_h2_conv_1 = 0\n",
    "        bob_h2_conv_1 = 0\n",
    "        #bs = 3 #batch_size\n",
    "        bs = int(total_len/16) #batch_size\n",
    "        messages, keys = gen_data(n=bs, msg_len=crypto_msg_len, key_len=crypto_key_len)\n",
    "        #test message to get the code complete\n",
    "        messages = np.array([\n",
    "                    [1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1], \n",
    "                    [0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1],\n",
    "                    [1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1]\n",
    "                   ])\n",
    "\n",
    "\n",
    "        #feed_dict={msg: messages, key: keys}\n",
    "        feed_dict={msg: plaintext_to_Alice, key: keys}\n",
    "\n",
    "        print( 'messages = \\n', plaintext_to_Alice)\n",
    "        print( 'keys = \\n', keys)\n",
    "\n",
    "        if network == 'alice':\n",
    "            print('plaintext_to_Alice = ', plaintext_to_Alice)\n",
    "            start_time = time.time()\n",
    "            _, decrypt_err, alice_output_1, alice_h2_conv_1  = sess.run([alice_optimizer, decrypt_err_alice, alice_output, alice_hidden], feed_dict = feed_dict)\n",
    "            end_time = time.time()\n",
    "            alice_encrypt_time = end_time-start_time\n",
    "            global alice_output_sign\n",
    "            alice_output_sign = alice_output_1\n",
    "            print('test alice_output (Signature) = \\n', alice_output_1)       \n",
    "            alice_decrypt_error = min(alice_decrypt_error, decrypt_err)\n",
    "            print('SOCK_SEND')\n",
    "            sock_send(alice_output_1)\n",
    "        elif network == 'bob':\n",
    "            print('SOCKET_RECV ==== \\n')\n",
    "            messages_sock = sock_recv(sock)\n",
    "            print(messages_sock)\n",
    "            feed_dict1 = {msg: messages_sock, key: keys}\n",
    "            start_time = time.time()\n",
    "            _, decrypt_err, bob_input_1, bob_output_1, bob_h2_conv_1 = sess.run([bob_optimizer, decrypt_err_bob, bob_input, bob_output, bob_hidden], feed_dict = feed_dict1)\n",
    "            end_time = time.time()\n",
    "            bob_decrypt_time = end_time-start_time\n",
    "            bob_input_1 = np.round(bob_input_1, 2)\n",
    "            bob_ouput_1 = np.round(bob_output_1, 2)\n",
    "            \n",
    "            print('test bob_input = \\n', bob_input_1)       \n",
    "            print('test bob_output = \\n',  bob_output_1)       \n",
    "            bob_decrypt_error = min(bob_decrypt_error, decrypt_err)\n",
    "        elif network == 'eve':\n",
    "            _, decrypt_err = sess.run([eve_optimizer, decrypt_err_eve], feed_dict = feed_dict)\n",
    "            eve_decrypt_error = min(eve_decrypt_error, decrypt_err)\n",
    "\n",
    "        return alice_decrypt_error, alice_encrypt_time, alice_h2_conv_1, bob_decrypt_error, bob_decrypt_time, bob_output_1, bob_h2_conv_1, eve_decrypt_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "def plot_results():\n",
    "    \"\"\"\n",
    "    Plot Lowest Errors achieved by Alice, Bob, and Eve per epoch\n",
    "    \"\"\"\n",
    "    sns.set_style(\"darkgrid\")\n",
    "    plt.plot(alice_errors)\n",
    "    plt.plot(bob_errors)\n",
    "    plt.plot(eve_errors)\n",
    "    plt.legend(['alice', 'bob', 'eve'])\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Lowest error achieved')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# TensorFlow Session "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Training Process... \n",
      "Training Alice, Epoch: 1  error:  0.000425669\n",
      "Training Bob, Epoch: 1  error:  0.0006918\n",
      "Training Eve, Epoch: 1  error:  0.254612\n",
      "Training Alice, Epoch: 2  error:  0.000466998\n",
      "Training Bob, Epoch: 2  error:  0.000184714\n",
      "Training Eve, Epoch: 2  error:  0.256473\n",
      "Training Alice, Epoch: 3  error:  2.75447e-05\n",
      "Training Bob, Epoch: 3  error:  8.63395e-05\n",
      "Training Eve, Epoch: 3  error:  0.256238\n",
      "Training Alice, Epoch: 4  error:  8.00124e-06\n",
      "Training Bob, Epoch: 4  error:  5.28852e-05\n",
      "Training Eve, Epoch: 4  error:  0.254151\n",
      "Training Alice, Epoch: 5  error:  2.81984e-06\n",
      "Training Bob, Epoch: 5  error:  4.139e-05\n",
      "Training Eve, Epoch: 5  error:  0.256104\n",
      "Training Alice, Epoch: 6  error:  8.55289e-07\n",
      "Training Bob, Epoch: 6  error:  3.4828e-05\n",
      "Training Eve, Epoch: 6  error:  0.25708\n",
      "Training Alice, Epoch: 7  error:  3.3695e-07\n",
      "Training Bob, Epoch: 7  error:  2.739e-05\n",
      "Training Eve, Epoch: 7  error:  0.238465\n",
      "Training Alice, Epoch: 8  error:  8.9407e-08\n",
      "Training Bob, Epoch: 8  error:  2.52482e-05\n",
      "Training Eve, Epoch: 8  error:  0.240662\n",
      "Training Alice, Epoch: 9  error:  6.39702e-08\n",
      "Training Bob, Epoch: 9  error:  2.45591e-05\n",
      "Training Eve, Epoch: 9  error:  0.24115\n",
      "Training Alice, Epoch: 10  error:  1.1969e-08\n",
      "Training Bob, Epoch: 10  error:  2.31954e-05\n",
      "Training Eve, Epoch: 10  error:  0.241455\n",
      "Training Alice, Epoch: 11  error:  4.72937e-09\n",
      "Training Bob, Epoch: 11  error:  2.03598e-05\n",
      "Training Eve, Epoch: 11  error:  0.239441\n",
      "Training Alice, Epoch: 12  error:  2.47383e-09\n",
      "Training Bob, Epoch: 12  error:  1.81538e-05\n",
      "Training Eve, Epoch: 12  error:  0.271912\n",
      "Training Alice, Epoch: 13  error:  0.0264892\n",
      "Training Bob, Epoch: 13  error:  1.82241e-05\n",
      "Training Eve, Epoch: 13  error:  0.272034\n",
      "Training Alice, Epoch: 14  error:  0.0164813\n",
      "Training Bob, Epoch: 14  error:  1.67204e-05\n",
      "Training Eve, Epoch: 14  error:  0.241141\n",
      "Training Alice, Epoch: 15  error:  0.00316654\n",
      "Training Bob, Epoch: 15  error:  1.57412e-05\n",
      "Training Eve, Epoch: 15  error:  0.241089\n",
      "Time taken for Training (seconds):  898.0463457107544\n"
     ]
    },
    {
     "data": {
      "image/png": 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2BebO/T2ys3MwePBQbN36PkaNGuO9/m9/24LevfuibdsCAMAjj8xESkoqFi58Fmlp6diw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\nwYgY+lrarTy1dicOp0qMUcetl0wiKT5mQN9zdG4yv732JK46u4jYGD2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ChJUEI0IIIYQI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\n+YOndp9/NgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e508caac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alice_errors_train =  [0.00034071674, 0.48095936, 0.00097656436, 0.00073938817, 0.00070087158, 0.072279774, 0.057399161, 0.12900548, 0.033768605, 0.0058621117, 0.0046170717, 5.4499113e-05, 3.6152604e-05, 2.2578446e-05, 1.3850595e-05, 6.2894483e-06, 2.5148611e-06, 0.00042566925, 0.00046699762, 2.7544716e-05, 8.0012396e-06, 2.819841e-06, 8.5528882e-07, 3.369496e-07, 8.9406967e-08, 6.3970219e-08, 1.196895e-08, 4.7293724e-09, 2.4738256e-09, 0.026489239, 0.016481312, 0.0031665408]\n",
      "bob_errors_train =  [0.48254484, 0.028678708, 0.0013088954, 0.00038241441, 0.00016329822, 0.00010313131, 8.1380698e-05, 5.8885529e-05, 5.1141596e-05, 3.9537881e-05, 3.6108511e-05, 2.5381143e-05, 1.5330905e-05, 1.1075083e-05, 3.7914479e-06, 8.4909868e-07, 0.00069179997, 0.00018471357, 8.6339547e-05, 5.288524e-05, 4.1389965e-05, 3.4827983e-05, 2.7390042e-05, 2.5248211e-05, 2.4559069e-05, 2.3195362e-05, 2.0359796e-05, 1.8153803e-05, 1.8224118e-05, 1.6720402e-05, 1.5741211e-05]\n",
      "eve_errors_train =  [0.028392296, 0.00011489117, 0.086343631, 0.00018022035, 0.08860077, 0.11878712, 0.11846939, 0.02838135, 0.027282789, 0.028198242, 0.028076172, 2.910383e-11, 1.4551915e-11, 0.0, 0.0, 0.2546123, 0.2564728, 0.25623831, 0.25415105, 0.25610366, 0.25708023, 0.23846534, 0.24066189, 0.24114999, 0.24145508, 0.23944092, 0.27191162, 0.27203369, 0.24114114, 0.24108897]\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "epochs = 15\n",
    "#sess = tf.InteractiveSession()\n",
    "sess = tf.Session()\n",
    "\n",
    "#with tf.Session() as sess:\n",
    "print('Starting Training Process... ')\n",
    "start_time = time.time()\n",
    "train(sess)\n",
    "end_time = time.time()\n",
    "print('Time taken for Training (seconds): ', end_time-start_time)\n",
    "plot_results()\n",
    "\n",
    "print('alice_errors_train = ', alice_errors)\n",
    "print('bob_errors_train = ', bob_errors)\n",
    "print('eve_errors_train = ', eve_errors)    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above section of the code for training the CNN does not have to be rerun if the user decides to input another message."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Alice\n",
      "messages = \n",
      " [[ 0.  1.  0.  1.  0.  1.  0.  0.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  1.  0.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  1.  1.  1.  0.  1.  1.  1.  1.  0.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  0.  1.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  0.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  1.  0.  0.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  1.  0.  0.  0.  0.  1.  1.  1.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  1.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  1.  0.  0.  0.  1.  1.  1.  1.  0.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  0.  0.  1.  1.  1.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  1.  1.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  0.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  1.  0.  0.  1.  0.  1.  1.  0.  1.  1.  1.  0.]\n",
      " [ 0.  1.  1.  1.  0.  1.  0.  0.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  1.  0.  1.  0.  1.  1.  0.  1.  1.  1.  0.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  1.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  1.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "keys = \n",
      " [[1 0 1 1 0 0 0 1 0 1 1 0 1 1 1 1]\n",
      " [1 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1]\n",
      " [0 0 0 1 0 1 1 1 1 0 0 0 1 0 1 1]\n",
      " [0 0 0 1 0 0 0 1 1 1 0 1 0 0 0 1]\n",
      " [1 1 1 0 1 0 1 0 1 1 0 1 1 0 0 1]\n",
      " [1 1 0 1 1 0 0 1 1 0 1 0 0 0 1 1]\n",
      " [1 1 0 0 1 0 1 1 1 1 0 0 0 0 0 0]\n",
      " [0 1 0 0 1 1 0 1 1 0 1 1 1 0 0 0]\n",
      " [0 1 1 0 1 1 0 1 1 0 0 0 0 0 1 1]\n",
      " [1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 0]\n",
      " [0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 1]\n",
      " [1 1 1 1 1 1 1 0 0 1 0 0 1 0 1 1]\n",
      " [0 1 1 0 0 0 0 1 1 0 0 0 1 1 0 0]\n",
      " [1 1 1 0 0 1 0 0 0 1 1 0 0 1 0 0]\n",
      " [0 1 1 1 1 1 0 1 0 1 0 1 0 1 1 0]\n",
      " [1 0 0 1 1 0 0 0 0 1 1 0 0 1 1 1]\n",
      " [1 1 1 1 0 0 0 0 1 1 0 0 1 0 0 1]\n",
      " [1 1 1 0 1 0 1 0 1 1 1 0 1 1 1 0]\n",
      " [1 1 1 0 1 0 0 1 1 1 0 0 0 0 1 1]\n",
      " [0 1 0 0 1 1 0 0 0 1 0 0 1 0 1 0]\n",
      " [0 1 0 1 1 1 0 1 0 0 1 1 1 1 0 0]\n",
      " [0 1 1 0 1 1 0 1 0 0 1 1 0 1 0 0]\n",
      " [0 1 1 1 1 0 1 1 0 1 0 0 0 1 1 1]\n",
      " [1 0 0 0 0 1 0 1 1 1 1 0 1 0 0 0]\n",
      " [0 1 0 1 0 0 1 0 1 0 1 1 1 1 0 0]\n",
      " [0 0 0 1 0 1 1 1 0 0 0 0 1 0 1 0]\n",
      " [1 0 1 1 0 1 0 0 1 1 1 1 0 1 0 1]\n",
      " [1 0 1 1 0 0 0 1 1 1 0 1 0 0 0 1]\n",
      " [0 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0]\n",
      " [1 0 1 0 0 0 0 1 1 1 0 1 0 0 1 1]\n",
      " [1 1 0 1 1 0 0 1 0 0 1 1 0 1 0 1]\n",
      " [0 0 1 1 1 1 0 0 0 1 0 0 0 1 0 0]\n",
      " [0 1 1 1 1 1 1 1 1 1 0 1 1 0 0 0]\n",
      " [0 1 0 0 1 0 1 0 1 1 1 0 1 1 1 1]\n",
      " [0 1 0 1 0 1 0 0 1 1 0 0 0 1 0 0]]\n",
      "plaintext_to_Alice =  [[ 0.  1.  0.  1.  0.  1.  0.  0.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  1.  0.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  1.  1.  1.  0.  1.  1.  1.  1.  0.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  0.  1.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  0.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  1.  0.  0.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  1.  0.  0.  0.  0.  1.  1.  1.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  1.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  1.  0.  0.  0.  1.  1.  1.  1.  0.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  0.  0.  1.  1.  1.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  1.  1.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  0.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  1.  0.  0.  1.  0.  1.  1.  0.  1.  1.  1.  0.]\n",
      " [ 0.  1.  1.  1.  0.  1.  0.  0.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  1.  0.  1.  0.  1.  1.  0.  1.  1.  1.  0.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  1.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  1.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "test alice_output (Signature) = \n",
      " [[ 0.          1.          0.          0.99999994  0.          0.99999988\n",
      "   0.          0.          0.          0.99999917  0.99999934  0.          0.\n",
      "   0.99999553  0.          0.99996525]\n",
      " [ 0.          0.99999785  0.99998289  0.          0.          0.\n",
      "   0.9999997   0.99999934  0.          0.99999893  0.99999988  0.\n",
      "   0.99999195  0.10601683  0.          0.        ]\n",
      " [ 0.          0.99998939  0.99994987  0.          0.99999988  0.99999911\n",
      "   0.99999917  0.          0.          0.9999994   0.99999958  0.\n",
      "   0.99999702  0.99998009  0.99999362  0.99984759]\n",
      " [ 0.          0.99999535  0.99998116  0.          0.99999988  0.99999911\n",
      "   0.          0.          0.          0.9999994   0.9999994   0.\n",
      "   0.99999821  0.99998885  0.99999577  0.99992549]\n",
      " [ 0.          0.99999106  0.99995989  0.          0.          0.99999982\n",
      "   0.99999851  0.99999976  0.          0.99999976  0.9999997   0.99999863\n",
      "   0.99963158  0.08182373  0.          0.99994755]\n",
      " [ 0.          0.          0.9999997   0.          0.          0.          0.\n",
      "   0.          0.          0.99999976  0.          0.99999982  0.\n",
      "   0.9999668   0.          0.        ]\n",
      " [ 0.          0.99999994  0.99998724  0.99999022  0.          0.          1.\n",
      "   0.          0.          0.9999997   0.99999958  0.          0.\n",
      "   0.12862219  0.          0.99998492]\n",
      " [ 0.          0.99999297  0.99996495  0.          0.99999988  0.99999917\n",
      "   0.99999803  0.          0.          0.99999982  0.9999994   0.99999917\n",
      "   0.          0.10915028  0.99999833  0.99995393]\n",
      " [ 0.          0.99999803  0.99998236  0.          0.          0.\n",
      "   0.9999997   0.99999976  0.          0.9999994   0.99999905  0.          0.\n",
      "   0.99999601  0.          0.99996853]\n",
      " [ 0.          0.99999911  0.99998647  0.          1.00000012  0.9999994\n",
      "   0.99999905  0.          0.          0.99999982  0.99999905  0.          0.\n",
      "   0.9999842   0.          0.        ]\n",
      " [ 0.          1.          0.9999947   0.99999505  0.          0.\n",
      "   0.99999976  0.9999994   0.          0.          1.          0.          0.\n",
      "   0.11542042  0.          0.        ]\n",
      " [ 0.          0.99999857  0.          0.          0.99999994  0.          0.\n",
      "   0.          0.          0.99999958  0.99999905  0.99999976  0.\n",
      "   0.99996376  0.          0.99995708]\n",
      " [ 0.          0.99999797  0.99998623  0.          0.9999997   0.99999964\n",
      "   0.          1.          0.          0.99999958  0.99999964  0.          0.\n",
      "   0.12909949  0.          0.99998474]\n",
      " [ 0.          0.99999654  0.99997985  0.          0.99999994  0.99999923\n",
      "   0.99999821  0.          0.          0.99999982  0.99999946  0.\n",
      "   0.99997783  0.11477271  0.          0.99996918]\n",
      " [ 0.          0.99999946  0.99995852  0.99999738  0.          0.99999964\n",
      "   0.          0.          0.          0.99999964  0.99999958  0.99999881\n",
      "   0.99967039  0.08560124  0.          0.99995673]\n",
      " [ 0.          0.          0.99999952  0.          0.          0.          0.\n",
      "   0.          0.          0.99999964  0.99999982  0.          0.\n",
      "   0.15265375  0.          0.99999118]\n",
      " [ 0.          0.99999571  0.99998236  0.          0.99999982  0.99999923\n",
      "   0.99999917  0.          0.          0.99999952  0.99999923  0.          0.\n",
      "   0.99998158  0.          0.        ]\n",
      " [ 0.          0.          0.9999994   0.          0.          0.          0.\n",
      "   0.          0.          0.99999988  0.          0.          0.\n",
      "   0.1597397   0.          0.99998558]\n",
      " [ 0.          0.99999988  0.99997455  0.99999386  0.          0.\n",
      "   0.99999988  0.          0.          0.99999982  0.99999917  0.99999964\n",
      "   0.          0.99955136  0.          0.        ]\n",
      " [ 0.          0.99999863  0.99998212  0.          0.99999982  0.          0.\n",
      "   1.          0.          0.9999997   0.99999934  0.          0.\n",
      "   0.99999249  0.99998176  0.        ]\n",
      " [ 0.          0.99999428  0.99997854  0.          0.99999988  0.          0.\n",
      "   1.          0.          0.99999946  0.99999964  0.          0.\n",
      "   0.16074547  0.99999589  0.99995387]\n",
      " [ 0.          0.99999785  0.99998593  0.          1.          0.          0.\n",
      "   1.          0.          0.99999946  0.99999952  0.          0.\n",
      "   0.13809714  0.          0.99998194]\n",
      " [ 0.          0.99999791  0.9999764   0.          0.9999994   0.99999982\n",
      "   0.          0.          0.          0.          0.99999994  0.          0.\n",
      "   0.13681599  0.          0.        ]\n",
      " [ 0.          0.99999964  0.          0.          1.          0.          0.\n",
      "   1.          0.          0.99999976  0.99999928  0.          0.99999917\n",
      "   0.99999106  0.99997646  0.        ]\n",
      " [ 0.          0.99999988  0.99998367  0.99999756  0.          0.99999988\n",
      "   0.          0.          0.          0.99999893  0.99999964  0.          0.\n",
      "   0.99999523  0.          0.99997646]\n",
      " [ 0.          0.99999803  0.99997842  0.          0.99999976  0.99999964\n",
      "   0.          0.          0.          0.99999958  0.99999964  0.\n",
      "   0.99999869  0.99989158  0.          0.        ]\n",
      " [ 0.          0.99998659  0.9999814   0.          0.99999976  0.          0.\n",
      "   1.          0.          0.99999928  0.99999917  0.          0.\n",
      "   0.99998313  0.99999118  0.9998706 ]\n",
      " [ 0.          0.99999523  0.99998504  0.          0.          0.99999982\n",
      "   0.          1.          0.          0.99999964  0.99999917  0.\n",
      "   0.99999845  0.9999817   0.99997938  0.        ]\n",
      " [ 0.          0.99999422  0.99997777  0.          0.          0.\n",
      "   0.99999958  0.99999988  0.          0.99999863  0.99999928  0.          0.\n",
      "   0.99999225  0.          0.99994266]\n",
      " [ 0.          0.          0.9999994   0.          0.          0.          0.\n",
      "   0.          0.          0.99999988  0.9999997   0.          0.99999481\n",
      "   0.13620006  0.          0.99998653]\n",
      " [ 0.          0.99999988  0.99998903  0.99999374  0.          0.\n",
      "   0.99999976  0.99999893  0.          0.          1.          0.          0.\n",
      "   0.12111679  0.          0.        ]\n",
      " [ 0.          0.99999851  0.99998492  0.          0.          0.\n",
      "   0.9999997   0.9999997   0.          0.99999964  0.9999994   0.\n",
      "   0.99999803  0.99998957  0.99999321  0.99986148]\n",
      " [ 0.          0.99997419  0.99995291  0.          0.99999946  0.99999928\n",
      "   0.          1.          0.          0.99999893  0.99999964  0.\n",
      "   0.99995315  0.11780289  0.          0.99993753]\n",
      " [ 0.          0.99998784  0.99994999  0.          0.99999994  0.99999833\n",
      "   0.99999928  0.          0.          0.99999851  0.9999994   0.          0.\n",
      "   0.99997395  0.99999416  0.99989164]\n",
      " [ 0.02020672  0.          0.99999976  0.          0.          0.          0.\n",
      "   1.          0.          0.          0.          0.          0.\n",
      "   0.12847698  0.          0.        ]]\n",
      "SOCK_SEND\n",
      "message =  [[ 0.          1.          0.          0.99999994  0.          0.99999988\n",
      "   0.          0.          0.          0.99999917  0.99999934  0.          0.\n",
      "   0.99999553  0.          0.99996525]\n",
      " [ 0.          0.99999785  0.99998289  0.          0.          0.\n",
      "   0.9999997   0.99999934  0.          0.99999893  0.99999988  0.\n",
      "   0.99999195  0.10601683  0.          0.        ]\n",
      " [ 0.          0.99998939  0.99994987  0.          0.99999988  0.99999911\n",
      "   0.99999917  0.          0.          0.9999994   0.99999958  0.\n",
      "   0.99999702  0.99998009  0.99999362  0.99984759]\n",
      " [ 0.          0.99999535  0.99998116  0.          0.99999988  0.99999911\n",
      "   0.          0.          0.          0.9999994   0.9999994   0.\n",
      "   0.99999821  0.99998885  0.99999577  0.99992549]\n",
      " [ 0.          0.99999106  0.99995989  0.          0.          0.99999982\n",
      "   0.99999851  0.99999976  0.          0.99999976  0.9999997   0.99999863\n",
      "   0.99963158  0.08182373  0.          0.99994755]\n",
      " [ 0.          0.          0.9999997   0.          0.          0.          0.\n",
      "   0.          0.          0.99999976  0.          0.99999982  0.\n",
      "   0.9999668   0.          0.        ]\n",
      " [ 0.          0.99999994  0.99998724  0.99999022  0.          0.          1.\n",
      "   0.          0.          0.9999997   0.99999958  0.          0.\n",
      "   0.12862219  0.          0.99998492]\n",
      " [ 0.          0.99999297  0.99996495  0.          0.99999988  0.99999917\n",
      "   0.99999803  0.          0.          0.99999982  0.9999994   0.99999917\n",
      "   0.          0.10915028  0.99999833  0.99995393]\n",
      " [ 0.          0.99999803  0.99998236  0.          0.          0.\n",
      "   0.9999997   0.99999976  0.          0.9999994   0.99999905  0.          0.\n",
      "   0.99999601  0.          0.99996853]\n",
      " [ 0.          0.99999911  0.99998647  0.          1.00000012  0.9999994\n",
      "   0.99999905  0.          0.          0.99999982  0.99999905  0.          0.\n",
      "   0.9999842   0.          0.        ]\n",
      " [ 0.          1.          0.9999947   0.99999505  0.          0.\n",
      "   0.99999976  0.9999994   0.          0.          1.          0.          0.\n",
      "   0.11542042  0.          0.        ]\n",
      " [ 0.          0.99999857  0.          0.          0.99999994  0.          0.\n",
      "   0.          0.          0.99999958  0.99999905  0.99999976  0.\n",
      "   0.99996376  0.          0.99995708]\n",
      " [ 0.          0.99999797  0.99998623  0.          0.9999997   0.99999964\n",
      "   0.          1.          0.          0.99999958  0.99999964  0.          0.\n",
      "   0.12909949  0.          0.99998474]\n",
      " [ 0.          0.99999654  0.99997985  0.          0.99999994  0.99999923\n",
      "   0.99999821  0.          0.          0.99999982  0.99999946  0.\n",
      "   0.99997783  0.11477271  0.          0.99996918]\n",
      " [ 0.          0.99999946  0.99995852  0.99999738  0.          0.99999964\n",
      "   0.          0.          0.          0.99999964  0.99999958  0.99999881\n",
      "   0.99967039  0.08560124  0.          0.99995673]\n",
      " [ 0.          0.          0.99999952  0.          0.          0.          0.\n",
      "   0.          0.          0.99999964  0.99999982  0.          0.\n",
      "   0.15265375  0.          0.99999118]\n",
      " [ 0.          0.99999571  0.99998236  0.          0.99999982  0.99999923\n",
      "   0.99999917  0.          0.          0.99999952  0.99999923  0.          0.\n",
      "   0.99998158  0.          0.        ]\n",
      " [ 0.          0.          0.9999994   0.          0.          0.          0.\n",
      "   0.          0.          0.99999988  0.          0.          0.\n",
      "   0.1597397   0.          0.99998558]\n",
      " [ 0.          0.99999988  0.99997455  0.99999386  0.          0.\n",
      "   0.99999988  0.          0.          0.99999982  0.99999917  0.99999964\n",
      "   0.          0.99955136  0.          0.        ]\n",
      " [ 0.          0.99999863  0.99998212  0.          0.99999982  0.          0.\n",
      "   1.          0.          0.9999997   0.99999934  0.          0.\n",
      "   0.99999249  0.99998176  0.        ]\n",
      " [ 0.          0.99999428  0.99997854  0.          0.99999988  0.          0.\n",
      "   1.          0.          0.99999946  0.99999964  0.          0.\n",
      "   0.16074547  0.99999589  0.99995387]\n",
      " [ 0.          0.99999785  0.99998593  0.          1.          0.          0.\n",
      "   1.          0.          0.99999946  0.99999952  0.          0.\n",
      "   0.13809714  0.          0.99998194]\n",
      " [ 0.          0.99999791  0.9999764   0.          0.9999994   0.99999982\n",
      "   0.          0.          0.          0.          0.99999994  0.          0.\n",
      "   0.13681599  0.          0.        ]\n",
      " [ 0.          0.99999964  0.          0.          1.          0.          0.\n",
      "   1.          0.          0.99999976  0.99999928  0.          0.99999917\n",
      "   0.99999106  0.99997646  0.        ]\n",
      " [ 0.          0.99999988  0.99998367  0.99999756  0.          0.99999988\n",
      "   0.          0.          0.          0.99999893  0.99999964  0.          0.\n",
      "   0.99999523  0.          0.99997646]\n",
      " [ 0.          0.99999803  0.99997842  0.          0.99999976  0.99999964\n",
      "   0.          0.          0.          0.99999958  0.99999964  0.\n",
      "   0.99999869  0.99989158  0.          0.        ]\n",
      " [ 0.          0.99998659  0.9999814   0.          0.99999976  0.          0.\n",
      "   1.          0.          0.99999928  0.99999917  0.          0.\n",
      "   0.99998313  0.99999118  0.9998706 ]\n",
      " [ 0.          0.99999523  0.99998504  0.          0.          0.99999982\n",
      "   0.          1.          0.          0.99999964  0.99999917  0.\n",
      "   0.99999845  0.9999817   0.99997938  0.        ]\n",
      " [ 0.          0.99999422  0.99997777  0.          0.          0.\n",
      "   0.99999958  0.99999988  0.          0.99999863  0.99999928  0.          0.\n",
      "   0.99999225  0.          0.99994266]\n",
      " [ 0.          0.          0.9999994   0.          0.          0.          0.\n",
      "   0.          0.          0.99999988  0.9999997   0.          0.99999481\n",
      "   0.13620006  0.          0.99998653]\n",
      " [ 0.          0.99999988  0.99998903  0.99999374  0.          0.\n",
      "   0.99999976  0.99999893  0.          0.          1.          0.          0.\n",
      "   0.12111679  0.          0.        ]\n",
      " [ 0.          0.99999851  0.99998492  0.          0.          0.\n",
      "   0.9999997   0.9999997   0.          0.99999964  0.9999994   0.\n",
      "   0.99999803  0.99998957  0.99999321  0.99986148]\n",
      " [ 0.          0.99997419  0.99995291  0.          0.99999946  0.99999928\n",
      "   0.          1.          0.          0.99999893  0.99999964  0.\n",
      "   0.99995315  0.11780289  0.          0.99993753]\n",
      " [ 0.          0.99998784  0.99994999  0.          0.99999994  0.99999833\n",
      "   0.99999928  0.          0.          0.99999851  0.9999994   0.          0.\n",
      "   0.99997395  0.99999416  0.99989164]\n",
      " [ 0.02020672  0.          0.99999976  0.          0.          0.          0.\n",
      "   1.          0.          0.          0.          0.          0.\n",
      "   0.12847698  0.          0.        ]]\n",
      "alice_errors_test =  0.00383225\n",
      "Testing Bob\n",
      "messages = \n",
      " [[ 0.  1.  0.  1.  0.  1.  0.  0.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  1.  0.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  1.  1.  1.  0.  1.  1.  1.  1.  0.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  0.  1.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  0.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  1.  0.  0.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  1.  0.  0.  0.  0.  1.  1.  1.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  1.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  1.  0.  0.  0.  1.  1.  1.  1.  0.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  0.  0.  1.  1.  1.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  1.  1.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  0.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  1.  0.  0.  1.  0.  1.  1.  0.  1.  1.  1.  0.]\n",
      " [ 0.  1.  1.  1.  0.  1.  0.  0.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  1.  0.  1.  0.  1.  1.  0.  1.  1.  1.  0.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  1.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  1.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "keys = \n",
      " [[1 1 1 0 1 0 1 0 1 1 1 1 1 0 0 1]\n",
      " [1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1]\n",
      " [0 1 1 1 1 0 0 1 1 0 1 0 0 0 0 0]\n",
      " [1 1 0 0 1 0 1 1 1 1 0 0 1 1 1 0]\n",
      " [1 0 1 1 0 0 1 0 0 0 0 0 1 1 0 1]\n",
      " [1 0 0 0 0 1 0 1 1 1 1 1 1 1 0 1]\n",
      " [1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 1]\n",
      " [0 1 1 0 0 0 1 0 0 0 1 0 1 0 0 1]\n",
      " [0 1 0 0 1 1 0 1 1 1 1 0 0 0 1 1]\n",
      " [0 0 0 1 0 1 1 1 0 1 0 1 0 1 0 1]\n",
      " [1 1 1 1 1 1 0 0 1 0 1 0 1 1 0 1]\n",
      " [0 1 1 0 0 0 1 1 1 0 0 0 0 0 1 1]\n",
      " [1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1]\n",
      " [1 0 1 0 0 0 0 1 1 0 1 1 1 0 0 1]\n",
      " [1 1 1 1 0 0 1 1 1 0 1 1 0 0 0 0]\n",
      " [1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 0]\n",
      " [1 0 0 1 0 1 1 1 1 0 0 0 1 1 1 0]\n",
      " [0 0 1 1 0 1 0 1 0 0 1 0 1 0 1 0]\n",
      " [0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 1]\n",
      " [1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 0]\n",
      " [0 0 1 0 1 0 1 1 1 0 0 1 0 1 0 0]\n",
      " [1 0 0 1 1 1 1 1 1 0 0 1 0 0 0 0]\n",
      " [0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 0]\n",
      " [0 0 1 1 0 1 1 0 0 1 0 1 1 0 1 0]\n",
      " [0 1 0 0 1 1 0 0 0 0 0 1 1 0 1 0]\n",
      " [1 0 0 0 0 1 1 0 1 1 1 1 1 0 1 0]\n",
      " [0 1 0 1 1 1 1 1 0 1 0 0 1 0 1 1]\n",
      " [0 0 1 0 0 1 0 1 1 0 1 1 1 1 0 0]\n",
      " [0 1 0 0 0 1 0 0 0 1 0 0 0 1 1 0]\n",
      " [1 1 1 1 1 0 1 1 1 1 0 1 0 1 0 1]\n",
      " [0 1 0 1 1 0 1 0 1 1 0 1 0 1 1 0]\n",
      " [0 0 1 1 0 1 0 0 1 0 0 0 1 0 0 0]\n",
      " [0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0]\n",
      " [1 1 0 1 0 0 0 1 1 1 1 0 1 1 1 1]\n",
      " [1 1 1 0 1 0 1 1 1 1 1 1 1 0 0 0]]\n",
      "SOCKET_RECV ==== \n",
      "\n",
      "waiting for a connection\n",
      "connection from ('127.0.0.1', 56110)\n",
      "received  [[ 0.          1.          0.          0.99999994  0.          0.99999988\n",
      "   0.          0.          0.          0.99999917  0.99999934  0.          0.\n",
      "   0.99999553  0.          0.99996525]\n",
      " [ 0.          0.99999785  0.99998289  0.          0.          0.\n",
      "   0.9999997   0.99999934  0.          0.99999893  0.99999988  0.\n",
      "   0.99999195  0.10601683  0.          0.        ]\n",
      " [ 0.          0.99998939  0.99994987  0.          0.99999988  0.99999911\n",
      "   0.99999917  0.          0.          0.9999994   0.99999958  0.\n",
      "   0.99999702  0.99998009  0.99999362  0.99984759]\n",
      " [ 0.          0.99999535  0.99998116  0.          0.99999988  0.99999911\n",
      "   0.          0.          0.          0.9999994   0.9999994   0.\n",
      "   0.99999821  0.99998885  0.99999577  0.99992549]\n",
      " [ 0.          0.99999106  0.99995989  0.          0.          0.99999982\n",
      "   0.99999851  0.99999976  0.          0.99999976  0.9999997   0.99999863\n",
      "   0.99963158  0.08182373  0.          0.99994755]\n",
      " [ 0.          0.          0.9999997   0.          0.          0.          0.\n",
      "   0.          0.          0.99999976  0.          0.99999982  0.\n",
      "   0.9999668   0.          0.        ]\n",
      " [ 0.          0.99999994  0.99998724  0.99999022  0.          0.          1.\n",
      "   0.          0.          0.9999997   0.99999958  0.          0.\n",
      "   0.12862219  0.          0.99998492]\n",
      " [ 0.          0.99999297  0.99996495  0.          0.99999988  0.99999917\n",
      "   0.99999803  0.          0.          0.99999982  0.9999994   0.99999917\n",
      "   0.          0.10915028  0.99999833  0.99995393]\n",
      " [ 0.          0.99999803  0.99998236  0.          0.          0.\n",
      "   0.9999997   0.99999976  0.          0.9999994   0.99999905  0.          0.\n",
      "   0.99999601  0.          0.99996853]\n",
      " [ 0.          0.99999911  0.99998647  0.          1.00000012  0.9999994\n",
      "   0.99999905  0.          0.          0.99999982  0.99999905  0.          0.\n",
      "   0.9999842   0.          0.        ]\n",
      " [ 0.          1.          0.9999947   0.99999505  0.          0.\n",
      "   0.99999976  0.9999994   0.          0.          1.          0.          0.\n",
      "   0.11542042  0.          0.        ]\n",
      " [ 0.          0.99999857  0.          0.          0.99999994  0.          0.\n",
      "   0.          0.          0.99999958  0.99999905  0.99999976  0.\n",
      "   0.99996376  0.          0.99995708]\n",
      " [ 0.          0.99999797  0.99998623  0.          0.9999997   0.99999964\n",
      "   0.          1.          0.          0.99999958  0.99999964  0.          0.\n",
      "   0.12909949  0.          0.99998474]\n",
      " [ 0.          0.99999654  0.99997985  0.          0.99999994  0.99999923\n",
      "   0.99999821  0.          0.          0.99999982  0.99999946  0.\n",
      "   0.99997783  0.11477271  0.          0.99996918]\n",
      " [ 0.          0.99999946  0.99995852  0.99999738  0.          0.99999964\n",
      "   0.          0.          0.          0.99999964  0.99999958  0.99999881\n",
      "   0.99967039  0.08560124  0.          0.99995673]\n",
      " [ 0.          0.          0.99999952  0.          0.          0.          0.\n",
      "   0.          0.          0.99999964  0.99999982  0.          0.\n",
      "   0.15265375  0.          0.99999118]\n",
      " [ 0.          0.99999571  0.99998236  0.          0.99999982  0.99999923\n",
      "   0.99999917  0.          0.          0.99999952  0.99999923  0.          0.\n",
      "   0.99998158  0.          0.        ]\n",
      " [ 0.          0.          0.9999994   0.          0.          0.          0.\n",
      "   0.          0.          0.99999988  0.          0.          0.\n",
      "   0.1597397   0.          0.99998558]\n",
      " [ 0.          0.99999988  0.99997455  0.99999386  0.          0.\n",
      "   0.99999988  0.          0.          0.99999982  0.99999917  0.99999964\n",
      "   0.          0.99955136  0.          0.        ]\n",
      " [ 0.          0.99999863  0.99998212  0.          0.99999982  0.          0.\n",
      "   1.          0.          0.9999997   0.99999934  0.          0.\n",
      "   0.99999249  0.99998176  0.        ]\n",
      " [ 0.          0.99999428  0.99997854  0.          0.99999988  0.          0.\n",
      "   1.          0.          0.99999946  0.99999964  0.          0.\n",
      "   0.16074547  0.99999589  0.99995387]\n",
      " [ 0.          0.99999785  0.99998593  0.          1.          0.          0.\n",
      "   1.          0.          0.99999946  0.99999952  0.          0.\n",
      "   0.13809714  0.          0.99998194]\n",
      " [ 0.          0.99999791  0.9999764   0.          0.9999994   0.99999982\n",
      "   0.          0.          0.          0.          0.99999994  0.          0.\n",
      "   0.13681599  0.          0.        ]\n",
      " [ 0.          0.99999964  0.          0.          1.          0.          0.\n",
      "   1.          0.          0.99999976  0.99999928  0.          0.99999917\n",
      "   0.99999106  0.99997646  0.        ]\n",
      " [ 0.          0.99999988  0.99998367  0.99999756  0.          0.99999988\n",
      "   0.          0.          0.          0.99999893  0.99999964  0.          0.\n",
      "   0.99999523  0.          0.99997646]\n",
      " [ 0.          0.99999803  0.99997842  0.          0.99999976  0.99999964\n",
      "   0.          0.          0.          0.99999958  0.99999964  0.\n",
      "   0.99999869  0.99989158  0.          0.        ]\n",
      " [ 0.          0.99998659  0.9999814   0.          0.99999976  0.          0.\n",
      "   1.          0.          0.99999928  0.99999917  0.          0.\n",
      "   0.99998313  0.99999118  0.9998706 ]\n",
      " [ 0.          0.99999523  0.99998504  0.          0.          0.99999982\n",
      "   0.          1.          0.          0.99999964  0.99999917  0.\n",
      "   0.99999845  0.9999817   0.99997938  0.        ]\n",
      " [ 0.          0.99999422  0.99997777  0.          0.          0.\n",
      "   0.99999958  0.99999988  0.          0.99999863  0.99999928  0.          0.\n",
      "   0.99999225  0.          0.99994266]\n",
      " [ 0.          0.          0.9999994   0.          0.          0.          0.\n",
      "   0.          0.          0.99999988  0.9999997   0.          0.99999481\n",
      "   0.13620006  0.          0.99998653]\n",
      " [ 0.          0.99999988  0.99998903  0.99999374  0.          0.\n",
      "   0.99999976  0.99999893  0.          0.          1.          0.          0.\n",
      "   0.12111679  0.          0.        ]\n",
      " [ 0.          0.99999851  0.99998492  0.          0.          0.\n",
      "   0.9999997   0.9999997   0.          0.99999964  0.9999994   0.\n",
      "   0.99999803  0.99998957  0.99999321  0.99986148]\n",
      " [ 0.          0.99997419  0.99995291  0.          0.99999946  0.99999928\n",
      "   0.          1.          0.          0.99999893  0.99999964  0.\n",
      "   0.99995315  0.11780289  0.          0.99993753]\n",
      " [ 0.          0.99998784  0.99994999  0.          0.99999994  0.99999833\n",
      "   0.99999928  0.          0.          0.99999851  0.9999994   0.          0.\n",
      "   0.99997395  0.99999416  0.99989164]\n",
      " [ 0.02020672  0.          0.99999976  0.          0.          0.          0.\n",
      "   1.          0.          0.          0.          0.          0.\n",
      "   0.12847698  0.          0.        ]]\n",
      "[[ 0.          1.          0.          0.99999994  0.          0.99999988\n",
      "   0.          0.          0.          0.99999917  0.99999934  0.          0.\n",
      "   0.99999553  0.          0.99996525]\n",
      " [ 0.          0.99999785  0.99998289  0.          0.          0.\n",
      "   0.9999997   0.99999934  0.          0.99999893  0.99999988  0.\n",
      "   0.99999195  0.10601683  0.          0.        ]\n",
      " [ 0.          0.99998939  0.99994987  0.          0.99999988  0.99999911\n",
      "   0.99999917  0.          0.          0.9999994   0.99999958  0.\n",
      "   0.99999702  0.99998009  0.99999362  0.99984759]\n",
      " [ 0.          0.99999535  0.99998116  0.          0.99999988  0.99999911\n",
      "   0.          0.          0.          0.9999994   0.9999994   0.\n",
      "   0.99999821  0.99998885  0.99999577  0.99992549]\n",
      " [ 0.          0.99999106  0.99995989  0.          0.          0.99999982\n",
      "   0.99999851  0.99999976  0.          0.99999976  0.9999997   0.99999863\n",
      "   0.99963158  0.08182373  0.          0.99994755]\n",
      " [ 0.          0.          0.9999997   0.          0.          0.          0.\n",
      "   0.          0.          0.99999976  0.          0.99999982  0.\n",
      "   0.9999668   0.          0.        ]\n",
      " [ 0.          0.99999994  0.99998724  0.99999022  0.          0.          1.\n",
      "   0.          0.          0.9999997   0.99999958  0.          0.\n",
      "   0.12862219  0.          0.99998492]\n",
      " [ 0.          0.99999297  0.99996495  0.          0.99999988  0.99999917\n",
      "   0.99999803  0.          0.          0.99999982  0.9999994   0.99999917\n",
      "   0.          0.10915028  0.99999833  0.99995393]\n",
      " [ 0.          0.99999803  0.99998236  0.          0.          0.\n",
      "   0.9999997   0.99999976  0.          0.9999994   0.99999905  0.          0.\n",
      "   0.99999601  0.          0.99996853]\n",
      " [ 0.          0.99999911  0.99998647  0.          1.00000012  0.9999994\n",
      "   0.99999905  0.          0.          0.99999982  0.99999905  0.          0.\n",
      "   0.9999842   0.          0.        ]\n",
      " [ 0.          1.          0.9999947   0.99999505  0.          0.\n",
      "   0.99999976  0.9999994   0.          0.          1.          0.          0.\n",
      "   0.11542042  0.          0.        ]\n",
      " [ 0.          0.99999857  0.          0.          0.99999994  0.          0.\n",
      "   0.          0.          0.99999958  0.99999905  0.99999976  0.\n",
      "   0.99996376  0.          0.99995708]\n",
      " [ 0.          0.99999797  0.99998623  0.          0.9999997   0.99999964\n",
      "   0.          1.          0.          0.99999958  0.99999964  0.          0.\n",
      "   0.12909949  0.          0.99998474]\n",
      " [ 0.          0.99999654  0.99997985  0.          0.99999994  0.99999923\n",
      "   0.99999821  0.          0.          0.99999982  0.99999946  0.\n",
      "   0.99997783  0.11477271  0.          0.99996918]\n",
      " [ 0.          0.99999946  0.99995852  0.99999738  0.          0.99999964\n",
      "   0.          0.          0.          0.99999964  0.99999958  0.99999881\n",
      "   0.99967039  0.08560124  0.          0.99995673]\n",
      " [ 0.          0.          0.99999952  0.          0.          0.          0.\n",
      "   0.          0.          0.99999964  0.99999982  0.          0.\n",
      "   0.15265375  0.          0.99999118]\n",
      " [ 0.          0.99999571  0.99998236  0.          0.99999982  0.99999923\n",
      "   0.99999917  0.          0.          0.99999952  0.99999923  0.          0.\n",
      "   0.99998158  0.          0.        ]\n",
      " [ 0.          0.          0.9999994   0.          0.          0.          0.\n",
      "   0.          0.          0.99999988  0.          0.          0.\n",
      "   0.1597397   0.          0.99998558]\n",
      " [ 0.          0.99999988  0.99997455  0.99999386  0.          0.\n",
      "   0.99999988  0.          0.          0.99999982  0.99999917  0.99999964\n",
      "   0.          0.99955136  0.          0.        ]\n",
      " [ 0.          0.99999863  0.99998212  0.          0.99999982  0.          0.\n",
      "   1.          0.          0.9999997   0.99999934  0.          0.\n",
      "   0.99999249  0.99998176  0.        ]\n",
      " [ 0.          0.99999428  0.99997854  0.          0.99999988  0.          0.\n",
      "   1.          0.          0.99999946  0.99999964  0.          0.\n",
      "   0.16074547  0.99999589  0.99995387]\n",
      " [ 0.          0.99999785  0.99998593  0.          1.          0.          0.\n",
      "   1.          0.          0.99999946  0.99999952  0.          0.\n",
      "   0.13809714  0.          0.99998194]\n",
      " [ 0.          0.99999791  0.9999764   0.          0.9999994   0.99999982\n",
      "   0.          0.          0.          0.          0.99999994  0.          0.\n",
      "   0.13681599  0.          0.        ]\n",
      " [ 0.          0.99999964  0.          0.          1.          0.          0.\n",
      "   1.          0.          0.99999976  0.99999928  0.          0.99999917\n",
      "   0.99999106  0.99997646  0.        ]\n",
      " [ 0.          0.99999988  0.99998367  0.99999756  0.          0.99999988\n",
      "   0.          0.          0.          0.99999893  0.99999964  0.          0.\n",
      "   0.99999523  0.          0.99997646]\n",
      " [ 0.          0.99999803  0.99997842  0.          0.99999976  0.99999964\n",
      "   0.          0.          0.          0.99999958  0.99999964  0.\n",
      "   0.99999869  0.99989158  0.          0.        ]\n",
      " [ 0.          0.99998659  0.9999814   0.          0.99999976  0.          0.\n",
      "   1.          0.          0.99999928  0.99999917  0.          0.\n",
      "   0.99998313  0.99999118  0.9998706 ]\n",
      " [ 0.          0.99999523  0.99998504  0.          0.          0.99999982\n",
      "   0.          1.          0.          0.99999964  0.99999917  0.\n",
      "   0.99999845  0.9999817   0.99997938  0.        ]\n",
      " [ 0.          0.99999422  0.99997777  0.          0.          0.\n",
      "   0.99999958  0.99999988  0.          0.99999863  0.99999928  0.          0.\n",
      "   0.99999225  0.          0.99994266]\n",
      " [ 0.          0.          0.9999994   0.          0.          0.          0.\n",
      "   0.          0.          0.99999988  0.9999997   0.          0.99999481\n",
      "   0.13620006  0.          0.99998653]\n",
      " [ 0.          0.99999988  0.99998903  0.99999374  0.          0.\n",
      "   0.99999976  0.99999893  0.          0.          1.          0.          0.\n",
      "   0.12111679  0.          0.        ]\n",
      " [ 0.          0.99999851  0.99998492  0.          0.          0.\n",
      "   0.9999997   0.9999997   0.          0.99999964  0.9999994   0.\n",
      "   0.99999803  0.99998957  0.99999321  0.99986148]\n",
      " [ 0.          0.99997419  0.99995291  0.          0.99999946  0.99999928\n",
      "   0.          1.          0.          0.99999893  0.99999964  0.\n",
      "   0.99995315  0.11780289  0.          0.99993753]\n",
      " [ 0.          0.99998784  0.99994999  0.          0.99999994  0.99999833\n",
      "   0.99999928  0.          0.          0.99999851  0.9999994   0.          0.\n",
      "   0.99997395  0.99999416  0.99989164]\n",
      " [ 0.02020672  0.          0.99999976  0.          0.          0.          0.\n",
      "   1.          0.          0.          0.          0.          0.\n",
      "   0.12847698  0.          0.        ]]\n",
      "test bob_input = \n",
      " [[ 0.    1.    0.   ...,  0.    0.    1.  ]\n",
      " [ 0.    1.    1.   ...,  1.    1.    1.  ]\n",
      " [ 0.    1.    1.   ...,  0.    0.    0.  ]\n",
      " ..., \n",
      " [ 0.    1.    1.   ...,  0.    1.    0.  ]\n",
      " [ 0.    1.    1.   ...,  1.    1.    1.  ]\n",
      " [ 0.02  0.    1.   ...,  0.    0.    0.  ]]\n",
      "test bob_output = \n",
      " [[  6.94036367e-04   9.99996185e-01   0.00000000e+00   9.99993861e-01\n",
      "    0.00000000e+00   9.99999881e-01   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   9.99999821e-01   0.00000000e+00   9.99999046e-01]\n",
      " [ -1.99079495e-05   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   9.99999762e-01\n",
      "    0.00000000e+00   1.00000000e+00   9.99999881e-01   0.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  1.99579913e-03   9.99999881e-01   9.99999702e-01   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   9.99999881e-01   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   1.00000000e+00   1.00000000e+00]\n",
      " [  2.31295382e-03   1.00000000e+00   9.99999881e-01   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000012e+00   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   1.00000000e+00   1.00000000e+00]\n",
      " [  1.11716927e-03   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00]\n",
      " [  5.67853276e-04   0.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   0.00000000e+00   9.99999762e-01\n",
      "    0.00000000e+00   1.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  6.24894979e-04   1.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00]\n",
      " [  6.79790857e-04   9.99999762e-01   1.00000000e+00   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   1.00000000e+00]\n",
      " [ -2.50399084e-04   9.99999881e-01   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   0.00000000e+00   9.99999762e-01]\n",
      " [  4.91618994e-04   9.99999762e-01   1.00000000e+00   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  4.94301261e-04   1.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  4.22298821e-04   1.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   9.99999702e-01\n",
      "    0.00000000e+00   1.00000000e+00   0.00000000e+00   9.99999166e-01]\n",
      " [ -5.18381479e-04   9.99999821e-01   9.99999881e-01   0.00000000e+00\n",
      "    1.00000000e+00   9.99999821e-01   0.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00]\n",
      " [  1.19924538e-04   9.99999702e-01   9.99999821e-01   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   9.99999881e-01   0.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00   0.00000000e+00   9.99999642e-01]\n",
      " [  1.10125495e-03   1.00000000e+00   1.00000000e+00   9.99999940e-01\n",
      "    0.00000000e+00   1.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00]\n",
      " [  6.42537954e-04   0.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00]\n",
      " [  5.80012624e-04   9.99999881e-01   9.99999404e-01   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  7.99298214e-05   0.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00]\n",
      " [  3.89635476e-04   1.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   9.99999881e-01\n",
      "    0.00000000e+00   1.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  3.55839671e-04   9.99999821e-01   9.99996126e-01   0.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00   0.00000000e+00   9.99999940e-01\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00]\n",
      " [  2.24769101e-04   9.99999762e-01   9.99995351e-01   0.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   1.00000000e+00]\n",
      " [ -1.02949096e-03   9.99999881e-01   9.99994814e-01   0.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00]\n",
      " [  2.24292264e-04   1.00000000e+00   9.99999881e-01   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  1.07967795e-03   1.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   9.99999881e-01   9.99999881e-01   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00]\n",
      " [  4.08887805e-04   1.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   9.99999821e-01   0.00000000e+00   9.99999702e-01]\n",
      " [  3.77535733e-04   9.99999821e-01   1.00000000e+00   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  5.05447329e-04   9.99999821e-01   9.99997973e-01   0.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00   0.00000000e+00   9.99999881e-01\n",
      "    0.00000000e+00   9.99999940e-01   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   1.00000000e+00]\n",
      " [  1.31809630e-03   9.99999940e-01   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   0.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   9.99999523e-01   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00]\n",
      " [ -3.79443110e-04   9.99999821e-01   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   0.00000000e+00   1.00000000e+00]\n",
      " [  1.05649198e-03   0.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   9.99999940e-01   0.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00]\n",
      " [  6.97255018e-04   1.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  7.04407576e-04   9.99999821e-01   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   1.00000000e+00   1.00000012e+00\n",
      "    0.00000000e+00   1.00000000e+00   9.99999940e-01   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   1.00000000e+00   1.00000000e+00]\n",
      " [ -5.72144811e-04   9.99999940e-01   9.99999762e-01   0.00000000e+00\n",
      "    9.99999881e-01   1.00000000e+00   0.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   9.99999821e-01   1.00000000e+00   0.00000000e+00\n",
      "    1.00000000e+00   0.00000000e+00   0.00000000e+00   9.99999523e-01]\n",
      " [  1.59436313e-03   9.99999881e-01   9.99999762e-01   0.00000000e+00\n",
      "    1.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   1.00000000e+00   1.00000000e+00   1.00000000e+00]\n",
      " [  2.16964215e-01   0.00000000e+00   9.99999940e-01   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   1.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n",
      "    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]]\n",
      "bob_errors_test =  0.00418969\n",
      "Testing Eve\n",
      "messages = \n",
      " [[ 0.  1.  0.  1.  0.  1.  0.  0.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  1.  0.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  1.  1.  1.  0.  1.  1.  1.  1.  0.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  0.  1.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  0.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  1.  0.  0.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  1.  0.  0.  0.  0.  1.  1.  1.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  1.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  1.  0.  0.  0.  1.  1.  1.  1.  0.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  0.  0.  1.  1.  1.  0.  1.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  1.  1.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  0.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  1.  0.  0.  1.  0.  1.  1.  0.  1.  1.  1.  0.]\n",
      " [ 0.  1.  1.  1.  0.  1.  0.  0.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.]\n",
      " [ 0.  1.  1.  0.  1.  0.  0.  1.  0.  1.  1.  0.  0.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  0.  1.  0.  1.  0.  1.  1.  0.  1.  1.  1.  0.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  0.  1.  0.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  0.  0.  0.  0.  0.]\n",
      " [ 0.  1.  1.  0.  0.  0.  1.  1.  0.  1.  1.  0.  1.  1.  1.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  0.  1.  0.  1.  1.  0.  1.  0.  0.  1.]\n",
      " [ 0.  1.  1.  0.  1.  1.  1.  0.  0.  1.  1.  0.  0.  1.  1.  1.]\n",
      " [ 0.  0.  1.  0.  0.  0.  0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]]\n",
      "keys = \n",
      " [[1 1 1 0 0 0 1 1 1 1 1 0 1 1 0 1]\n",
      " [0 1 0 1 1 0 1 0 0 1 0 1 0 1 0 1]\n",
      " [1 1 1 0 1 0 0 0 1 0 0 1 0 1 1 0]\n",
      " [1 1 1 0 1 1 1 1 0 0 0 0 0 1 1 0]\n",
      " [0 0 1 0 0 1 1 1 1 1 0 1 1 1 0 0]\n",
      " [0 0 0 1 1 0 0 0 0 0 1 1 1 1 0 0]\n",
      " [0 1 1 0 1 0 0 0 1 0 1 1 0 0 1 1]\n",
      " [1 0 1 1 1 1 1 0 1 1 0 0 1 0 0 1]\n",
      " [0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0]\n",
      " [1 1 0 0 1 1 0 1 1 1 0 0 1 0 1 1]\n",
      " [1 1 0 1 0 1 0 0 0 1 0 1 1 1 1 0]\n",
      " [1 0 0 1 1 1 0 0 1 0 0 0 1 0 1 0]\n",
      " [0 0 1 0 1 1 1 0 0 0 1 1 0 0 0 1]\n",
      " [1 0 1 1 0 1 0 0 1 0 1 1 0 1 1 0]\n",
      " [1 1 1 1 0 0 1 1 0 1 1 1 0 0 0 1]\n",
      " [1 0 0 0 0 0 1 1 0 1 1 0 1 0 1 0]\n",
      " [1 1 1 0 0 0 1 1 1 1 0 0 1 0 1 0]\n",
      " [1 0 1 1 0 1 0 1 0 0 0 1 1 0 1 0]\n",
      " [1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0]\n",
      " [1 1 0 0 0 0 1 1 1 0 0 0 0 1 0 1]\n",
      " [1 0 0 1 0 1 1 0 1 0 0 1 0 0 1 1]\n",
      " [1 1 0 1 0 0 1 1 0 0 0 1 1 0 0 0]\n",
      " [0 1 1 0 0 0 0 1 1 1 1 1 0 0 0 1]\n",
      " [1 1 0 0 0 0 1 1 0 0 0 0 1 1 0 0]\n",
      " [0 1 0 1 0 0 0 1 0 0 1 1 0 0 0 1]\n",
      " [1 1 0 1 0 1 1 1 0 0 1 0 0 0 1 1]\n",
      " [1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1]\n",
      " [1 1 0 1 0 1 0 1 1 1 1 0 1 1 0 0]\n",
      " [0 1 1 1 0 0 0 1 0 0 0 1 1 1 0 1]\n",
      " [1 0 0 1 0 0 1 1 0 0 0 0 1 1 1 1]\n",
      " [1 1 1 1 0 1 0 0 1 0 0 0 1 1 1 1]\n",
      " [0 1 1 1 0 1 1 0 1 0 0 1 0 0 1 0]\n",
      " [0 0 0 1 0 1 0 0 0 1 1 1 0 1 0 1]\n",
      " [0 1 0 0 0 0 1 0 0 1 1 0 0 1 0 1]\n",
      " [0 1 1 1 1 0 1 0 1 1 0 1 0 1 1 0]]\n",
      "eve_errors_test =  0.210714\n",
      "Time taken for Testing (seconds):  0.11504316329956055\n",
      "Alice Computed Signature*********\n",
      "\tBinary =  01010100011001010110001101101000011011100110111101101100011011110110011101111001001000000101010001110010011000010110111001110011011000110110010101101110011001000111001100100000010010000111010101101101011000010110111001101001011101000111100100100000011000010110111001100100001000000100000101110010011101000110100101100110011010010110001101101001011000010110110000100000010010010110111001110100011001010110110001101100011010010110011101100101011011100110001101100101001000000110100101110011001000000110001101101111011011010110100101101110011001110010000100000000\n",
      "\tHex =  546563686e6f6c6f6779205472616e7363656e64732048756d616e69747920616e64204172746966696369616c20496e74656c6c6967656e636520697320636f6d696e672100\n",
      "Bob Computed Signature*********\n",
      "\tBinary =  01010100011001010110001101101000011011100110111101101100011011110110011101111001001000000101010001110010011000010110111001110011011000110110010101101110011001000111001100100000010010000111010101101101011000010110111001101001011101000111100100100000011000010110111001100100001000000100000101110010011101000110100101100110011010010110001101101001011000010110110000100000010010010110111001110100011001010110110001101100011010010110011101100101011011100110001101100101001000000110100101110011001000000110001101101111011011010110100101101110011001110010000100000000\n",
      "\tHex =  546563686e6f6c6f6779205472616e7363656e64732048756d616e69747920616e64204172746966696369616c20496e74656c6c6967656e636520697320636f6d696e672100\n",
      "Alice and Bob Signature (32 bytes) Verification SUCCESS!\n"
     ]
    }
   ],
   "source": [
    "import binascii\n",
    "def stringize(data):\n",
    "    b1_temp = np.rint(data)\n",
    "    b1_temp = b1_temp.ravel();\n",
    "    b1_temp = np.abs(b1_temp)\n",
    "    b2_temp  = np.array2string(b1_temp)\n",
    "    b2_temp = b2_temp.strip('[')\n",
    "    b2_temp = b2_temp.strip(']')\n",
    "    b2_temp = b2_temp.replace(\" \", \"\")\n",
    "    b2_temp = b2_temp.replace(\".\", \"\")\n",
    "    b2_temp = b2_temp.replace(\"\\n\", \"\")\n",
    "    print('\\tBinary = ',  b2_temp)\n",
    "    s1 =  ''.join([ \"%x\"%int(bin,2) for bin in b2_temp.split() ])\n",
    "    #Pad for SHA256 (32 Bytes = 64 digits)\n",
    "    s1 = s1.zfill(64)\n",
    "\n",
    "    return s1\n",
    "\n",
    "#Test the Neural Crypto model\n",
    "start_time = time.time()\n",
    "print( 'Testing Alice' )\n",
    "alice_loss_test, alice_sign_time, alice_h2_conv_1,_,_, _, _,_ = test('alice', sess)\n",
    "print('alice_errors_test = ', alice_loss_test)\n",
    "print( 'Testing Bob' )\n",
    "_,_,_,bob_loss_test, bob_sign_time, bob_output_1,bob_h2_conv_1,_ = test('bob', sess)\n",
    "print('bob_errors_test = ', bob_loss_test)    \n",
    "print( 'Testing Eve' )\n",
    "_, _,_,_,_,_,_,eve_loss_test = test('eve', sess)\n",
    "print('eve_errors_test = ', eve_loss_test)\n",
    "end_time = time.time()\n",
    "print('Time taken for Testing (seconds): ', end_time-start_time)\n",
    "\n",
    "#print('alice_output_sign=', alice_output_sign)\n",
    "print('Alice Computed Signature*********')\n",
    "alice_sign = stringize(alice_output_sign)\n",
    "print('\\tHex = ', alice_sign)\n",
    "print('Bob Computed Signature*********')\n",
    "bob_sign = stringize(bob_output_1)\n",
    "print('\\tHex = ', bob_sign)\n",
    "\n",
    "if (alice_sign == bob_sign):\n",
    "    print('Alice and Bob Signature (32 bytes) Verification SUCCESS!')\n",
    "else:\n",
    "    print('Alice and Bob Signature (32 bytes) Verification ERROR')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Performance Benchmarking with cryptography.fernet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fernet is an implementation of symmetric encryption. It uses AES for encryption and SHA256 for authentication, with initialization vectors generated using os.urandom(). The following code compares SHA256 to NCA in terms of time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Install the Cryptography module\n",
    "pip install cryptography"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cryptography.fernet SHA256 sign/verify sessions\n",
      "------------------------------------------------\n",
      "*********Performance of NCA versus Cryptography.Fernet********************\n",
      "Time taken for Alice to Sign the Plain Text (seconds):  0.015861988067626953\n",
      "Time taken for Fernet to Sign the Plain Text (seconds):  0.0002117156982421875\n"
     ]
    }
   ],
   "source": [
    "from cryptography.fernet import Fernet\n",
    "key = Fernet.generate_key()\n",
    "cipher_suite = Fernet(key)\n",
    "#Preprocess the array from input block above\n",
    "v2 = []\n",
    "for i in range(0, len(b1)):\n",
    "    v2.append(int(b1[i]))\n",
    "    \n",
    "print('Cryptography.fernet SHA256 sign/verify sessions')\n",
    "print('------------------------------------------------')\n",
    "\n",
    "from cryptography.hazmat.backends import default_backend\n",
    "from cryptography.hazmat.primitives import hashes, hmac\n",
    "h = hmac.HMAC(key, hashes.SHA256(), backend=default_backend())\n",
    "start_time = time.time()\n",
    "h.update(bytes(v2))\n",
    "hash_val = h.finalize() \n",
    "end_time = time.time()\n",
    "fernet_sign_time = end_time - start_time\n",
    "\n",
    "\n",
    "print('*********Performance of NCA versus Cryptography.Fernet********************')\n",
    "print('Time taken for Alice to Sign the Plain Text (seconds): ', alice_sign_time)\n",
    "print('Time taken for Fernet to Sign the Plain Text (seconds): ', fernet_sign_time)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, I have demonstrated the unique capability of neural networks to ensure integrity and authenticity through digital signatures comparable to SHA256. \n",
    "\n",
    "Future work also includes implementing the sockets over Internet Protocol, instead of localhost. In addition, other goals include implementing Eve and her socket as part of the crypto-pipeline and transmitting signatures appended to the plaintext across the sockets. It would also be interesting to inspect if Eve is able to compute the signature of a plaintext message through the Eve_Hidden layer and reach similar results as Bob and Alice. \n",
    "\n",
    "Another important field that I would like to explore is the combination of quantum and neural cryptography. Quantum cryptography involves altering the contents of a message in transit as soon as the attacker (Eve) sees it; this guarantees the integrity of the message. For example, quantum cryptography could be used as a potential channel for transmission of messages encrypted through NCA.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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