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. We present how CycleGAN can be made compatible with discrete data
and train in a stable way. We then prove that the technique used in CipherGAN avoids the …