Paper 2023/006

Exploring multi-task learning in the context of two masked AES implementations

Thomas Marquet, Universität Klagenfurt
Elisabeth Oswald, Universität Klagenfurt

This paper investigates different ways of applying multi-task learning in the context of two masked AES implementations (via the ASCAD-r and ASCAD-v2 databases). Enabled by multi-task learning, we propose novel architectures that significantly increase the consistency and performance of deep neural networks in a context where the attacker can not access the randomness of the countermeasures during profiling. Our work provides a wide range of experiments to understand the benefits of multi-task strategies against the current single-task state of the art. We show that multi-task learning is significantly more performant than single-task models on all our experiments. Furthermore, such strategies achieve novel milestones against protected implementations as we propose a new best attack on ASCAD-r and ASCAD-v2, along with models that defeat for the first time all masks of the affine masking on ASCAD-v2.

Available format(s)
Attacks and cryptanalysis
Publication info
Side Channel AttacksMaskingDeep LearningMulti-task Learning
Contact author(s)
thomas marquet @ aau at
elisabeth oswald @ aau at
2023-09-10: last of 3 revisions
2023-01-02: received
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      author = {Thomas Marquet and Elisabeth Oswald},
      title = {Exploring multi-task learning in the context of two masked AES implementations},
      howpublished = {Cryptology ePrint Archive, Paper 2023/006},
      year = {2023},
      note = {\url{}},
      url = {}
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