Proceedings of the 8th International Conference on Historical Cryptology (HistoCrypt 2025)

Selle kollektsiooni püsiv URIhttps://hdl.handle.net/10062/109727

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Decipherment of Historical Manuscripts with Unknown or Rare Writings: The DESCRYPT Project
(Tartu University Library, 2025) Megyesi, Beáta; Fornés, Alicia; Héder, Mihály; Heil, Raphaela; Kopal, Nils; Láng, Benedek; Rattenborg, Rune; Waldispühl, Michelle; Antal, Eugen; Marák, Pavol
We present a newly funded research program, DESCRYPT, aimed at deciphering and analyzing historical texts with rare or unknown scripts. The project leverages advancements in computational linguistics, artificial intelligence (AI), and image processing, alongside traditional philological methods, to develop innovative tools for transcription, recognition, and interpretation of historical writings with rare/unknown scripts, including ciphertexts. By integrating interdisciplinary expertise, DESCRYPT addresses the challenges posed by complex and undeciphered texts, preserving and unlocking the secrets of our shared cultural heritage.
Enhancing Classical Cipher Type Detection: Prompt Engineering with Common LLMs versus Usage of Custom AI Models
(Tartu University Library, 2025) Bastian, Maik; Esslinger, Bernhard; Hermann, Eckehard; Kopal, Nils; Lampesberger, Harald; Antal, Eugen; Marák, Pavol
In the field of cryptography, identifying the type of cipher used in an encrypted message is crucial to effective cryptanalysis. Thus far, from a machine learning perspective, this classification problem has been tackled using specifically designed models, such as the Neural Cipher Identifier (NCID), which require data generation and model training capabilities. The recent advent of Large Language Models (LLMs) raises the following question: Can this classification problem be approached more effectively through prompt engineering? This paper explores various generic strategies for prompt engineering, such as chain-of-thought and in-context learning, by evaluating thousands of generated prompts for classical ciphers using open-source LLMs (on an Nvidia DGX system) and ChatGPT (via a browser interface and API). The classification accuracies achieved through these prompting techniques are compared with those obtained by NCID. Although our findings indicate that NCID still significantly outperforms the use of LLMs for cipher-type detection, the latter offers a more accessible approach to cryptography tasks. Both methods can benefit from domain-specific knowledge in cryptanalysis, highlighting the importance of expert input in improving initial classifications and handling complex cipher types.

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Sirvi Proceedings of the 8th International Conference on Historical Cryptology (HistoCrypt 2025) Autor "Kopal, Nils" järgi