Design and Analysis of a CNN-Transformer-Based Differential Distinguisher for ARX Ciphers
Year: 2026
Type: article
Abstract: Neural distinguishers are commonly developed and evaluated using cipher-specific data representations and model configurations. This paper presents a common CNN-Transformer differential-distinguisher architecture for the evaluated ARX cipher SPECK and the ARX-related addition–shift–XOR ciphers TEA and XTEA. The framework combines a supervised front-end purification gate, multi-scale convolutional feature extraction, multiple-ciphertext-pair representation, and self-attention-based aggregation. The purification gate is trained only on the training split and is treated as the first stage of an end-to-end classifier; samples rejected by the gate are not removed from the test-set evaluation. The same backbone architecture is trained separately for each evaluated cipher and round configuration. The resulting classifiers achieve accuracies of 98.64% for 7-round SPECK32/64 and 90.76% for 10-round TEA, and retain distinguishing capability for 5-cycle XTEA. These results demonstrate applicability across the evaluated word-oriented ciphers; they do not constitute an end-to-end key-recovery attack.
Source: Electronics
Authors: Lei Zhang, Yuxuan Wu, Jiao Lei, Quanrun Lv, Chaoen Xiao, Jianxin Wang, Ding Ding, Ruipeng Hong
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7203839010