论文标题
序列到序列模型是否可以破解替换密码？
Can Sequence-to-Sequence Models Crack Substitution Ciphers?
论文作者
论文摘要
历史密码的解剖是一个具有挑战性的问题。目标明文的语言可能未知，并且密文可能会有很多噪音。假设已知宣传语言，则使用梁搜索和神经语言模型来为给定密码的候选授权假设为候选授权假设评分。我们提出了一个端到端的多语言模型，用于求解简单的替换密码。我们在合成和真实的历史密码上测试了我们的模型，并表明我们提出的方法可以在不明确的语言识别的情况下破译文本，同时仍然对噪声保持稳健。
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.
