Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Ettedgui, Raphaël"'
Randomized smoothing is the dominant standard for provable defenses against adversarial examples. Nevertheless, this method has recently been proven to suffer from important information theoretic limitations. In this paper, we argue that these limita
Externí odkaz:
http://arxiv.org/abs/2206.01715
In this paper, we study the problem of consistency in the context of adversarial examples. Specifically, we tackle the following question: can surrogate losses still be used as a proxy for minimizing the $0/1$ loss in the presence of an adversary tha
Externí odkaz:
http://arxiv.org/abs/2205.10022
Is there a classifier that ensures optimal robustness against all adversarial attacks? This paper answers this question by adopting a game-theoretic point of view. We show that adversarial attacks and defenses form an infinite zero-sum game where cla
Externí odkaz:
http://arxiv.org/abs/2002.11565