Paper 2026/1530 Original: eprint.iacr.org/2026/1530 ↗

Rich Input Representations in Neural Differential Cryptanalysis: A Taxonomy and Survey

Abstract

Neural differential distinguishers have become an active research direction in​ symmetric-key cryptanalysis since the introduction of deep-learning-based attacks on​ round-reduced SPECK. Early neural distinguishers typically used a single ciphertext pair​ or ciphertext difference as input. Recent studies, however, show that richer input​ representations can substantially affect the information available to the classifier, the data​ cost of each labeled sample, and the relevance of the distinguisher to practical attacks.​ Examples include multi-pair, multi-difference, matrix-style, multi-round,​ structured-encoding, and score-aggregation based inputs.​ This paper provides a taxonomy and survey of rich input representations in neural​ differential cryptanalysis. We introduce a representation-centric framework that describes​ an input representation by its difference set, number of observations per sample, sharing​ structure, encoding function, and ciphertext cost. Using this framework, we organize​ existing works into representation families and compare their motivations, benefits, and​ limitations. We also argue that representation-rich distinguishers require cost-aware​ evaluation: fixed-sample comparisons and fixed-ciphertext comparisons answer different​ questions and may lead to different conclusions. Finally, we identify open problems related​ to automated representation search, theoretical explanation of representation gain,​ cipher-family transferability, interpretability, reproducibility, and key-recovery integration.​ The survey highlights that rich input representations should be treated as first-class​ cryptanalytic design choices rather than secondary implementation details.