Can Sequence-to-Sequence Models Crack Substitution Ciphers?

2020/12/30 by Nada Aldarrab, Jonathan May, Aldarrab, Nada +1 · 2 citations
Computer Science · #Authorship Attribution and Profiling #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Topic Modeling #cs.CL

paper · pdf · doi:10.48550/arxiv.2012.15229

ACL 2021 main conference

openalex publication_date 2020/12/30 · arxiv created 2021/06/01 · arxiv updated 2021/06/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• ALICE: An Interpretable Neural Architecture for Generalization in Substitution Ciphers
• Can Transformers Break Encryption Schemes via In-Context Learning?
• ICL CIPHERS: Quantifying "Learning" in In-Context Learning via Substitution Ciphers

Related

• Segmenting Numerical Substitution Ciphers
• Solving Historical Dictionary Codes with a Neural Language Model
• ALICE: An Interpretable Neural Architecture for Generalization in Substitution Ciphers
• All Code, No Thought: Current Language Models Struggle to Reason in Ciphered Language
• CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation