Improved Differential Neural Distinguishers for SHA-3-256 and Ascon-Hash256
Year: 2026
Type: article
Abstract: The sponge construction serves as a fundamental design framework for hash functions and authenticated encryption algorithms. In differential cryptanalysis of large-state permutations underlying such algorithms, conventional approaches are constrained by state size and diffusion speed. The feature extraction capability of deep learning offers a potential alternative to mitigate these limitations. To improve the distinguishing performance of differential neural distinguishers against sponge-based algorithms, a methodology integrating data construction and network architecture optimization is proposed. Specifically, a multi-sample triplet input format is designed to preserve differential characteristics, and a convolutional block attention module is introduced to capture long-range dependencies along both the channel and spatial dimensions within the large-state permutation. Experimental evaluations were conducted on the Keccak and Ascon algorithms. For Keccak, the maximum distinguishable round number was identified as 3. At this round number, Keccak-p achieved full distinguishability (100% accuracy), while the sponge-based SHA-3-256 attained a distinguishing accuracy of 99.99%, improving upon the previous best result by 0.95 percentage points. For Ascon, the maximum distinguishable round number was 4, where Ascon-p achieved an accuracy of 54.85% with 64 sample pairs—the highest reported accuracy for this setting—while delivering comparable performance at the matched 32-pair setting (53.40% vs. 53.54% in prior work) with approximately one-twelfth of the training epochs; under the same setting, the sponge-based Ascon-Hash256 achieved an accuracy of 53.06%. These findings demonstrate the effectiveness of the proposed framework in enhancing neural distinguisher accuracy against sponge-based algorithms and offer an analytical approach for empirical security evaluation, with results qualitatively consistent with the indifferentiability bound of the sponge construction.
Source: Electronics
Authors: Lulu Guo, Ming Duan, Yuefei Zhu
Institution: PLA Information Engineering University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7212871138