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TensorFlow implementation of CipherGAN
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README.md

CipherGAN

Implementation of CipherGAN, used to obtain the results detailed in Unsupervised Cipher-Cracking Using Neural Networks.
Authors: Aidan N. Gomez, Sīcōng Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Łukasz Kaiser

Citing this work

@inproceedings{
    n.2018unsupervised,
    title={Unsupervised Cipher Cracking Using Discrete {GAN}s},
    author={Aidan N. Gomez and Sicong Huang and Ivan Zhang and Bryan M. Li and Muhammad Osama and Lukasz Kaiser},
    booktitle={International Conference on Learning Representations},
    year={2018},
    url={https://openreview.net/forum?id=BkeqO7x0-},
}

Running this code

Call pip install -r CipherGAN/requirements.txt to install all dependencies.

Generating Data

We make use of data generators to generate the TFRecords that are used for training. Of particular note is cipher_generator, which may be used to generate data for the shift and Vigenère ciphers that were tested in the paper.

Sample Call

The settings for the included generators are passed as flags. For example, to generate a word-level Vigenère Cipher (key:CDE) on the Brown Corpus with a sample length of 200, call:

python CipherGAN/data/data_generators/cipher_generator.py \
  --cipher=vigenere \
  --vigenere_key=345 \
  --percentage_training=0.9 \
  --corpus=brown \
  --vocab_size=200 \
  --test_name=vigenere345-brown200-eval \
  --train_name=vigenere345-brown200-train \
  --output_dir=tmp/data \
  --vocab_filename=vigenere345_brown200_vocab.txt

Training

All training can be performed by calling train.py. Training requires the TFRecords generated by the included generators.

Sample Call

Please refer to the flags accepted by train.py for a full set of options.

python -m CipherGAN.train \
  --output_dir=runs/vig345 \
  --test_name="vigenere345-brown200-eval*" \
  --train_name="vigenere345-brown200-train*" \
  --hparam_sets=vigenere_brown_vocab_200

Contributing

We'd love to accept your contributions to this project. Please feel free to open an issue, or submit a pull request as necessary. If you have implementations of this repository in other ML frameworks, please reach out so we may highlight them here.

Acknowledgements

Our thanks to Michal Wiszniewski for his assistance in developing this codebase.
In addition, this repository borrows and builds upon code from:

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