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We give an overview of the development of European historical cipher keys originating from early Modern times. We describe the nature and the structure of the keys with a special focus on the nomenclatures. We analyze what was encoded and how and take into account chronological and regional differences. The study is based on the analysis of over 1,600 cipher keys, collected from archives and libraries in 10 European countries. We show that historical cipher keys evolved over time and became more secure, shown by the symbol set used for encoding, the code length and the code types presented in the key, the size of the nomenclature, as well as the diversity and complexity of linguistic entities that are chosen to be encoded.

In this paper, we study the impact of language models (LM) on decipherment of historical homophonic substitution ciphers. In particular, we investigate if decipherment by using hill-climbing and simulated annealing can benefit from LMs generated from historical texts in general and century-specific texts in particular. We carry out experiments on homophonic substitution ciphers with English and German as plaintext languages. We take into account ciphertext length as well as n-gram size of the LMs. We compare the results on decipherment based on historical LMs with large LMs generated from modern texts. The results show that using historical LMs in decipherment of homophonic substitution ciphers leads to significantly better performance on ciphertext produced in the 17th century or earlier, and century-specific language models yield better results on longer and older ciphertexts.

The cross-disciplinary nature of historical cryptology involves the challenge to find a terminology that is both consistent and accepted across the different disciplines and applicable in the single fields. In this paper, we propose a terminology based on concise principles developed by an interdisciplinary group of researchers. We present terms prominent in the study of historical cryptology, define them, and illustrate their usage. Our goal is to initiate and/or continue the discussion of how we use various terms for different types of historical encrypted sources, and their study. Our hope is that this paper will contribute to consistent and systematic usage of terms in the HistoCrypt community.

Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. 

In historical encrypted sources we can find encrypted text sequences, also called ciphertext, as well as non-encrypted cleartexts written in a known language. While most of the cryptanalysis focuses on the decryption of ciphertext, cleartext is often overlooked although it can give us important clues about the historical interpretation and contextualisation of the manuscript. In this paper, we investigate to what extent we can automatically distinguish cleartext from ciphertext in historical ciphers and to what extent we are able to identify its language. The problem is challenging as cleartext sequences in ciphers are often short, up to a few words, in different languages due to historical code-switching. To identify the sequences and the language(s), we chose a rule-based approach and run 7 different models using historical language models on various ciphertexts.

Hand-written Text Recognition techniques withthe aim to automatically identify and transcribehand-written text have been applied to histor-ical sources including ciphers. In this paper,we compare the performance of two machinelearning architectures, an unsupervised methodbased on clustering and a deep learning methodwith few-shot learning. Both models are testedon seen and unseen data from historical cipherswith different symbol sets consisting of varioustypes of graphic signs. We compare the modelsand highlight their differences in performance,with their advantages and shortcomings.

We report recent developments of the DE-CODE database aimed for the system-atic collection and annotation of encryptedsources: ciphertexts, keys and related doc-uments. We released a new, more func-tional graphical user interface, revisedsome metadata features and enlarged thecollection and tripled its size.