Paper 2020/757

Understanding Methodology for Efficient CNN Architectures in Profiling Attacks

Gabriel Zaid, Lilian Bossuet, Amaury Habrard, and Alexandre Venelli


The use of deep learning in side-channel analysis has been more and more prominent recently. In particular, Convolution Neural Networks (CNN) are very efficient tools to extract the secret information from side-channel traces. Previous work regarding the use of CNN in side-channel has been mostly proposed through practical results. Zaid et al. have proposed a theoretical methodology in order to better understand the convolutional part of CNN and to understand how to construct an efficient CNN in the side-channel context [ZBHV19]. The proposal of Zaid et al. has been recently questioned by [WAGP20]. However this revisit is based on wrong assumptions and misinterpretations. Hence, many of the claims of [WAGP20] are unfounded regarding [ZBHV19]. In this paper, we clear out the potential misunderstandings brought by [WAGP20] and explain more thoroughly the contributions of [ZBHV19].

Available format(s)
Secret-key cryptography
Publication info
Preprint. MINOR revision.
Side-Channel AttacksDeep LearningNetwork ArchitectureWeight VisualizationEntanglement
Contact author(s)
gabriel zaid @ univ-st-etienne fr
2020-06-21: received
Short URL
Creative Commons Attribution


      author = {Gabriel Zaid and Lilian Bossuet and Amaury Habrard and Alexandre Venelli},
      title = {Understanding Methodology for Efficient CNN Architectures in Profiling Attacks},
      howpublished = {Cryptology ePrint Archive, Paper 2020/757},
      year = {2020},
      note = {\url{}},
      url = {}
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