2 citations · 3 across the 4 of their papers we have counts for
5 papers
How to choose your best allies for a transferable attack?
Thibault Maho, Seyed-Mohsen Moosavi-Dezfooli, Teddy Furon
The transferability of adversarial examples is a key issue in the security of deep neural networks. The possibility of an adversarial example crafted for a source model fooling ano…
FBI: Fingerprinting models with Benign Inputs
Thibault Maho, Teddy Furon, Erwan Le Merrer
Recent advances in the fingerprinting of deep neural networks detect instances of models, placed in a black-box interaction scheme. Inputs used by the fingerprinting protocols are…
Randomized Smoothing under Attack: How Good is it in Pratice?
Thibault Maho, Teddy Furon, Erwan Le Merrer
Randomized smoothing is a recent and celebrated solution to certify the robustness of any classifier. While it indeed provides a theoretical robustness against adversarial attacks,…
RoBIC: A benchmark suite for assessing classifiers robustness
Thibault Maho, Benoît Bonnet, Teddy Furon +1
Many defenses have emerged with the development of adversarial attacks. Models must be objectively evaluated accordingly. This paper systematically tackles this concern by proposin…
SurFree: a fast surrogate-free black-box attack
Thibault Maho, Teddy Furon, Erwan Le Merrer
Machine learning classifiers are critically prone to evasion attacks. Adversarial examples are slightly modified inputs that are then misclassified, while remaining perceptively cl…