4 papers · 1 filter
Pulling Back the Curtain: Unsupervised Adversarial Detection via Contrastive Auxiliary Networks
Eylon Mizrahi, Raz Lapid, Moshe Sipper
Deep learning models are widely employed in safety-critical applications yet remain susceptible to adversarial attacks -- imperceptible perturbations that can significantly degrade…
Patch of Invisibility: Naturalistic Physical Black-Box Adversarial Attacks on Object Detectors
Raz Lapid, Eylon Mizrahi, Moshe Sipper
Adversarial attacks on deep learning models have received increased attention in recent years. Work in this area has mostly focused on gradient-based techniques, so-called 'white-b…
On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective
Tal Alter, Raz Lapid, Moshe Sipper
Kolmogorov-Arnold Networks (KANs) have recently emerged as a novel approach to function approximation, demonstrating remarkable potential in various domains. Despite their theoreti…
Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors
Raz Lapid, Almog Dubin, Moshe Sipper
This paper presents RADAR-Robust Adversarial Detection via Adversarial Retraining-an approach designed to enhance the robustness of adversarial detectors against adaptive attacks,…