Theoretical evidence for adversarial robustness through randomization
Pinot, Rafaël; Meunier, Laurent; Araújo, Alexandre; Kashima, Hisashi; Yger, Florian; Gouy-Pailler, Cedric; Atif, Jamal (2019), Theoretical evidence for adversarial robustness through randomization, 33rd Conference on Neural Information Processing Systems (NIPS 2019), 2019-12, Vancouver, CANADA
TypeCommunication / Conférence
External document linkhttps://hal.archives-ouvertes.fr/hal-02892188
Conference title33rd Conference on Neural Information Processing Systems (NIPS 2019)
MetadataShow full item record
Abstract (EN)This paper investigates the theory of robustness against adversarial attacks. It focuses on the family of randomization techniques that consist in injecting noise in the network at inference time. These techniques have proven effective in many contexts, but lack theoretical arguments. We close this gap by presenting a theoretical analysis of these approaches, hence explaining why they perform well in practice. More precisely, we make two new contributions. The first one relates the randomization rate to robustness to adversarial attacks. This result applies for the general family of exponential distributions, and thus extends and unifies the previous approaches. The second contribution consists in devising a new upper bound on the adversarial generalization gap of randomized neural networks. We support our theoretical claims with a set of experiments.
Subjects / KeywordsMachine Learning
Showing items related by title and author.
Pinot, Rafaël; Meunier, Laurent; Araújo, Alexandre; Kashima, Hisashi; Yger, Florian; Gouy-Pailler, Cedric; Atif, Jamal (2019) Communication / Conférence
Pinot, Rafaël; Meunier, Laurent; Yger, Florian; Gouy-Pailler, Cedric; Chevaleyre, Yann; Atif, Jamal (2022) Article accepté pour publication ou publié