Paper 2026/1136

Local Constraints Behind Fourier Analysis of Neural Distinguishers for SPECK32/64

Abstract

Neural distinguishers for ARX ciphers can exploit information beyond classical difference distributions and several interpretability frameworks have been proposed. In this paper, we study two frameworks for SPECK32/64 by connecting their viewpoints: local constraints of modular addition and Fourier analysis of trained neural distinguishers. We show that the dominant Fourier parities of a raw-pair differential neural distinguisher can be rewritten in the local variables associated with the last modular addition. This representation separates value-dependent variant differential-linear terms from difference-dependent traditional terms, and explains their biases through specific local constraints and branch effects. We further extend the analysis to a boomerang right-quartet setting. We construct a neural distinguisher whose input is only the original ciphertext pair, while positive and negative samples are matched with respect to the observed ciphertext difference. Fourier analysis of this distinguisher reveals dominant value-dependent parities. We trace these terms to borrow synchronization in the first inverse step of the lower boomerang characteristic, yielding specific local conditions. Our results indicate that the dominant Fourier features learned in these settings are observable projections of concrete carry or borrow constraints of the ARX operation.