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Conference Papers Year : 2019

L 1-norm double backpropagation adversarial defense


Adversarial examples are a challenging open problem for deep neural networks. We propose in this paper to add a penalization term that forces the decision function to be at in some regions of the input space, such that it becomes, at least locally, less sensitive to attacks. Our proposition is theoretically motivated and shows on a rst set of carefully conducted experiments that it behaves as expected when used alone, and seems promising when coupled with adversarial training.
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Dates and versions

hal-02049020 , version 1 (05-03-2019)



Ismaïla Seck, Gaëlle Loosli, Stephane Canu. L 1-norm double backpropagation adversarial defense. ESANN - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2019, Bruges, France. ⟨hal-02049020⟩
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