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Unsupervised Cipher Cracking Using Discrete GANs

ICLR 2018 • Aidan N. Gomez • Sicong Huang • Ivan Zhang • Bryan M. Li • Muhammad Osama • Lukasz Kaiser

This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext. We demonstrate that CipherGAN is capable of cracking language data enciphered using shift and Vigenere ciphers to a high degree of fidelity and for vocabularies much larger than previously achieved... (read more)

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METHOD	TYPE
Batch Normalization	Normalization
Residual Connection	Skip Connections
PatchGAN	Discriminators
ReLU	Activation Functions
Tanh Activation	Activation Functions
Residual Block	Skip Connection Blocks
Instance Normalization	Normalization
Convolution	Convolutions
Leaky ReLU	Activation Functions
Sigmoid Activation	Activation Functions
GAN Least Squares Loss	Loss Functions
Cycle Consistency Loss	Loss Functions
CycleGAN	Generative Models

Unsupervised Cipher Cracking Using Discrete GANs

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