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CipherSniffer: Classifying Cipher Types

arXiv 2306.08116 ↗First posted 13 Jun 2023cs.CL cs.CR cs.LGcited by 0

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Abstract (arXiv)

Ciphers are a powerful tool for encrypting communication. There are many different cipher types, which makes it computationally expensive to solve a cipher using brute force. In this paper, we frame the decryption task as a classification problem. We first create a dataset of transpositions, substitutions, text reversals, word reversals, sentence shifts, and unencrypted text. Then, we evaluate the performance of various tokenizer-model combinations on this task.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 11 references that are in the map. First posted on arXiv 13 Jun 2023.

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References · 4

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,989
  4. 2016Robsut Wrod Reocginiton via semi-Character Recurrent Neural Networkcited by 18

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