In this article, we propose a detailed analysis and thorough explanations of the inherent workings of this new neural distinguisher. First, we studied the classified sets and tried to find some patterns that could guide us to better understand Gohr's results. We show with experiments that the neural distinguisher generally relies on the differential distribution on the ciphertext pairs, but also on the differential distribution in penultimate and antepenultimate rounds. In order to validate our findings, we construct a distinguisher for speck cipher based on pure cryptanalysis, without using any neural network, that achieves basically the same accuracy as Gohr's neural distinguisher and with the same efficiency (therefore improving over previous non-neural based distinguishers).
Moreover, as another approach, we provide a machine learning-based distinguisher that strips down Gohr's deep neural network to a bare minimum. We are able to remain very close to Gohr's distinguishers' accuracy using simple standard machine learning tools. In particular, we show that Gohr's neural distinguisher is in fact inherently building a very good approximation of the Differential Distribution Table (DDT) of the cipher during the learning phase, and using that information to directly classify ciphertext pairs. This result allows a full interpretability of the distinguisher and represents on its own an interesting contribution towards interpretability of deep neural networks.
Finally, we propose some method to improve over Gohr's work and possible new neural distinguishers settings. All our results are confirmed with experiments we have been conducted on speck block cipher (source code available online).
Category / Keywords: secret-key cryptography / Differential Cryptanalysis, SPECK, Machine Learning, Deep Neural Networks, Interpretability Original Publication (with minor differences): IACR-EUROCRYPT-2021 Date: received 4 Mar 2021, last revised 22 Mar 2021 Contact author: adrien002 at e ntu edu sg,dagerault@gmail com,thomas peyrin@ntu edu sg,quanquan001@e ntu edu sg Available format(s): PDF | BibTeX Citation Version: 20210322:192754 (All versions of this report) Short URL: ia.cr/2021/287