35 citations · 148 across the 31 of their papers we have counts for
3 papers · 1 filter
Advocating for Multiple Defense Strategies against Adversarial Examples
Alexandre Araujo, Laurent Meunier, Rafael Pinot +1
It has been empirically observed that defense mechanisms designed to protect neural networks against adversarial examples offer poor performance against adve…
SPEED: Secure, PrivatE, and Efficient Deep learning
Arnaud Grivet Sébert, Rafael Pinot, Martin Zuber +2
We introduce a deep learning framework able to deal with strong privacy constraints. Based on collaborative learning, differential privacy and homomorphic encryption, the proposed…
Randomization matters. How to defend against strong adversarial attacks
Rafael Pinot, Raphael Ettedgui, Geovani Rizk +2
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 adv…