Full Code of ankeshanand/neural-cryptography-tensorflow for AI

Repository: ankeshanand/neural-cryptography-tensorflow
Branch: master
Commit: 8691f580ff07
Files: 10
Total size: 10.7 KB

Directory structure:
gitextract_iprf4wtj/

├── .gitignore
├── LICENSE.txt
├── README.md
├── main.py
├── requirements.txt
└── src/
    ├── __init__.py
    ├── config.py
    ├── layers.py
    ├── model.py
    └── utils.py

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FILE CONTENTS
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================================================
FILE: .gitignore
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# Virtual Env
env/

# PyCharm files
.idea/

# Byte-compiled files
*.pyc



================================================
FILE: LICENSE.txt
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MIT License

Copyright (c) 2016 Ankesh Anand

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


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FILE: README.md
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**Note**: This repository is no longer maintained. 

# Adversarial Neural Cryptography in [TensorFlow](https://github.com/tensorflow/tensorflow)

A Tensorflow Flow implementation of Google Brain's recent paper ([Learning to Protect Communications with Adversarial Neural Cryptography.](https://arxiv.org/pdf/1610.06918v1.pdf))

Two Neural Networks, Alice and Bob learn to communicate secretly with each other, in presence of an adversary Eve.

![Setup](assets/diagram.png)

## Pre-requisites

* TensorFlow 
* Seaborn (for plots)
* Numpy

## Usage 
First, ensure you have the dependencies installed.

    $ pip install -r requirements.txt

To train the neural networks, run the `main.py` script.

    $ python main.py --msg-len 32 --epochs 50
    
    
## Attribution / Thanks

* carpedm20's DCGAN [implementation](https://github.com/carpedm20/DCGAN-tensorflow) in TensorFlow. 
* Liam's [implementation](https://github.com/nlml/adversarial-neural-crypt) of Adversarial Neural Cryptography in Theano. 

## Citing Code
If you want to cite this code in your work, refer to the following DOI:

[![DOI](https://zenodo.org/badge/73807045.svg)](https://zenodo.org/badge/latestdoi/73807045)

## License

MIT


================================================
FILE: main.py
================================================
import tensorflow as tf

from argparse import ArgumentParser
from src.model import CryptoNet
from src.config import *


def build_parser():
    parser = ArgumentParser()

    parser.add_argument('--msg-len', type=int,
                        dest='msg_len', help='message length',
                        metavar='MSG_LEN', default=MSG_LEN)

    parser.add_argument('--learning-rate', type=float,
                        dest='learning_rate',
                        help='learning rate (default %(default)s)',
                        metavar='LEARNING_RATE', default=LEARNING_RATE)

    parser.add_argument('--epochs', type=int,
                        dest='epochs', help='Number of Epochs in Adversarial Training',
                        metavar='EPOCHS', default=NUM_EPOCHS)

    parser.add_argument('--batch-size', type=int,
                        dest='batch_size', help='batch size',
                        metavar='BATCH_SIZE', default=BATCH_SIZE)

    return parser


def main():
    parser = build_parser()
    options = parser.parse_args()

    with tf.Session() as sess:
        crypto_net = CryptoNet(sess, msg_len=options.msg_len, epochs=options.epochs,
                               batch_size=options.batch_size, learning_rate=options.learning_rate)

        crypto_net.train()

if __name__ == '__main__':
    main()


================================================
FILE: requirements.txt
================================================
cycler==0.10.0
funcsigs==1.0.2
matplotlib==2.1.0
mock==2.0.0
numpy==1.13.3
pandas==0.21.0
pbr==3.1.1
protobuf==3.5.0.post1
pyparsing==2.1.10
python-dateutil==2.6.0
pytz==2017.3
scipy==1.0.0
seaborn==0.8.1
six==1.11.0
tensorflow==1.4.0


================================================
FILE: src/__init__.py
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================================================
FILE: src/config.py
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FILTERS = [
    [4, 1, 2],
    [2, 2, 4],
    [1, 4, 4],
    [1, 4, 1]
]

MSG_LEN = 16
KEY_LEN = 16
BATCH_SIZE = 512
NUM_EPOCHS = 60
LEARNING_RATE = 0.0008


================================================
FILE: src/layers.py
================================================
import tensorflow as tf
from config import FILTERS


def conv1d(input_, filter_shape, stride, name="conv1d"):
    with tf.variable_scope(name):
        w = tf.get_variable('w', shape=filter_shape,
                            initializer=tf.contrib.layers.xavier_initializer())
        conv = tf.nn.conv1d(input_, w, stride, padding='SAME')

        return conv


def conv_layer(hidden_layer_output, name):
    h0 = tf.nn.relu(conv1d(hidden_layer_output, FILTERS[0], stride=1, name=name+'_h0_conv'))
    h1 = tf.nn.relu(conv1d(h0, FILTERS[1], stride=2, name=name+'_h1_conv'))
    h2 = tf.nn.relu(conv1d(h1, FILTERS[2], stride=1, name=name+'_h2_conv'))
    h3 = tf.nn.tanh(conv1d(h2, FILTERS[3], stride=1, name=name+'_h3_conv'))

    return h3


================================================
FILE: src/model.py
================================================
import tensorflow as tf
import numpy as np

import matplotlib
# OSX fix
matplotlib.use('TkAgg')

import matplotlib.pyplot as plt
import seaborn as sns

from layers import conv_layer
from config import *
from utils import init_weights, gen_data


class CryptoNet(object):
    def __init__(self, sess, msg_len=MSG_LEN, batch_size=BATCH_SIZE,
                 epochs=NUM_EPOCHS, learning_rate=LEARNING_RATE):
        """
        Args:
            sess: TensorFlow session
            msg_len: The length of the input message to encrypt.
            key_len: Length of Alice and Bob's private key.
            batch_size: Minibatch size for each adversarial training
            epochs: Number of epochs in the adversarial training
            learning_rate: Learning Rate for Adam Optimizer
        """

        self.sess = sess
        self.msg_len = msg_len
        self.key_len = self.msg_len
        self.N = self.msg_len
        self.batch_size = batch_size
        self.epochs = epochs
        self.learning_rate = learning_rate

        self.build_model()

    def build_model(self):
        # Weights for fully connected layers
        self.w_alice = init_weights("alice_w", [2 * self.N, 2 * self.N])
        self.w_bob = init_weights("bob_w", [2 * self.N, 2 * self.N])
        self.w_eve1 = init_weights("eve_w1", [self.N, 2 * self.N])
        self.w_eve2 = init_weights("eve_w2", [2 * self.N, 2 * self.N])

        # Placeholder variables for Message and Key
        self.msg = tf.placeholder("float", [None, self.msg_len])
        self.key = tf.placeholder("float", [None, self.key_len])

        # Alice's network
        # FC layer -> Conv Layer (4 1-D convolutions)
        self.alice_input = tf.concat([self.msg, self.key],1)
        self.alice_hidden = tf.nn.sigmoid(tf.matmul(self.alice_input, self.w_alice))
        self.alice_hidden = tf.expand_dims(self.alice_hidden, 2)
        self.alice_output = tf.squeeze(conv_layer(self.alice_hidden, "alice"))

        # Bob's network
        # FC layer -> Conv Layer (4 1-D convolutions)
        self.bob_input = tf.concat([self.alice_output, self.key],1)
        self.bob_hidden = tf.nn.sigmoid(tf.matmul(self.bob_input, self.w_bob))
        self.bob_hidden = tf.expand_dims(self.bob_hidden, 2)
        self.bob_output = tf.squeeze(conv_layer(self.bob_hidden, "bob"))

        # Eve's network
        # FC layer -> FC layer -> Conv Layer (4 1-D convolutions)
        self.eve_input = self.alice_output
        self.eve_hidden1 = tf.nn.sigmoid(tf.matmul(self.eve_input, self.w_eve1))
        self.eve_hidden2 = tf.nn.sigmoid(tf.matmul(self.eve_hidden1, self.w_eve2))
        self.eve_hidden2 = tf.expand_dims(self.eve_hidden2, 2)
        self.eve_output = tf.squeeze(conv_layer(self.eve_hidden2, "eve"))

    def train(self):
        # Loss Functions
        self.decrypt_err_eve = tf.reduce_mean(tf.abs(self.msg - self.eve_output))
        self.decrypt_err_bob = tf.reduce_mean(tf.abs(self.msg - self.bob_output))
        self.loss_bob = self.decrypt_err_bob + (1. - self.decrypt_err_eve) ** 2.

        # Get training variables corresponding to each network
        self.t_vars = tf.trainable_variables()
        self.alice_or_bob_vars = [var for var in self.t_vars if 'alice_' in var.name or 'bob_' in var.name]
        self.eve_vars = [var for var in self.t_vars if 'eve_' in var.name]

        # Build the optimizers
        self.bob_optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(
            self.loss_bob, var_list=self.alice_or_bob_vars)
        self.eve_optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(
            self.decrypt_err_eve, var_list=self.eve_vars)

        self.bob_errors, self.eve_errors = [], []

        # Begin Training
        tf.global_variables_initializer().run()
        for i in range(self.epochs):
            iterations = 2000

            print 'Training Alice and Bob, Epoch:', i + 1
            bob_loss, _ = self._train('bob', iterations)
            self.bob_errors.append(bob_loss)

            print 'Training Eve, Epoch:', i + 1
            _, eve_loss = self._train('eve', iterations)
            self.eve_errors.append(eve_loss)

        self.plot_errors()

    def _train(self, network, iterations):
        bob_decrypt_error, eve_decrypt_error = 1., 1.

        bs = self.batch_size
        # Train Eve for two minibatches to give it a slight computational edge
        if network == 'eve':
            bs *= 2

        for i in range(iterations):
            msg_in_val, key_val = gen_data(n=bs, msg_len=self.msg_len, key_len=self.key_len)

            if network == 'bob':
                _, decrypt_err = self.sess.run([self.bob_optimizer, self.decrypt_err_bob],
                                               feed_dict={self.msg: msg_in_val, self.key: key_val})
                bob_decrypt_error = min(bob_decrypt_error, decrypt_err)

            elif network == 'eve':
                _, decrypt_err = self.sess.run([self.eve_optimizer, self.decrypt_err_eve],
                                               feed_dict={self.msg: msg_in_val, self.key: key_val})
                eve_decrypt_error = min(eve_decrypt_error, decrypt_err)

        return bob_decrypt_error, eve_decrypt_error

    def plot_errors(self):
        """
        Plot Lowest Decryption Errors achieved by Bob and Eve per epoch
        """
        sns.set_style("darkgrid")
        plt.plot(self.bob_errors)
        plt.plot(self.eve_errors)
        plt.legend(['bob', 'eve'])
        plt.xlabel('Epoch')
        plt.ylabel('Lowest Decryption error achieved')
        plt.show()



================================================
FILE: src/utils.py
================================================
import numpy as np
import tensorflow as tf
from config import *


# Function to generate n random messages and keys
def gen_data(n=BATCH_SIZE, msg_len=MSG_LEN, key_len=KEY_LEN):
    return (np.random.randint(0, 2, size=(n, msg_len))*2-1), \
           (np.random.randint(0, 2, size=(n, key_len))*2-1)


# Xavier Glotrot initialization of weights
def init_weights(name, shape):
    return tf.get_variable(name, shape=shape,
                           initializer=tf.contrib.layers.xavier_initializer())