Spaces:
Running
Running
File size: 2,667 Bytes
0b8359d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
# Learning to Protect Communications with Adversarial Neural Cryptography
This is a slightly-updated model used for the paper
["Learning to Protect Communications with Adversarial Neural
Cryptography"](https://arxiv.org/abs/1610.06918).
> We ask whether neural networks can learn to use secret keys to protect
> information from other neural networks. Specifically, we focus on ensuring
> confidentiality properties in a multiagent system, and we specify those
> properties in terms of an adversary. Thus, a system may consist of neural
> networks named Alice and Bob, and we aim to limit what a third neural
> network named Eve learns from eavesdropping on the communication between
> Alice and Bob. We do not prescribe specific cryptographic algorithms to
> these neural networks; instead, we train end-to-end, adversarially.
> We demonstrate that the neural networks can learn how to perform forms of
> encryption and decryption, and also how to apply these operations
> selectively in order to meet confidentiality goals.
This code allows you to train encoder/decoder/adversary network triplets
and evaluate their effectiveness on randomly generated input and key
pairs.
## Prerequisites
The only software requirements for running the encoder and decoder is having
TensorFlow installed.
Requires TensorFlow r0.12 or later.
## Training and evaluating
After installing TensorFlow and ensuring that your paths are configured
appropriately:
```
python train_eval.py
```
This will begin training a fresh model. If and when the model becomes
sufficiently well-trained, it will reset the Eve model multiple times
and retrain it from scratch, outputting the accuracy thus obtained
in each run.
## Model differences from the paper
The model has been simplified slightly from the one described in
the paper - the convolutional layer width was reduced by a factor
of two. In the version in the paper, there was a nonlinear unit
after the fully-connected layer; that nonlinear has been removed
here. These changes improve the robustness of training. The
initializer for the convolution layers has switched to the
`tf.contrib.layers default` of `xavier_initializer` instead of
a simpler `truncated_normal`.
## Contact information
This model repository is maintained by David G. Andersen
([dave-andersen](https://github.com/dave-andersen)).
|