Can Sequence-to-Sequence Models Crack Substitution Ciphers?

Nada Aldarrab, Jonathan May


Abstract
Decipherment of historical ciphers is a challenging problem. The language of the target plaintext might be unknown, and ciphertext can have a lot of noise. State-of-the-art decipherment methods use beam search and a neural language model to score candidate plaintext hypotheses for a given cipher, assuming the plaintext language is known. We propose an end-to-end multilingual model for solving simple substitution ciphers. We test our model on synthetic and real historical ciphers and show that our proposed method can decipher text without explicit language identification while still being robust to noise.
Anthology ID:
2021.acl-long.561
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7226–7235
Language:
URL:
https://aclanthology.org/2021.acl-long.561
DOI:
10.18653/v1/2021.acl-long.561
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.561.pdf