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, while maintaining classifier performance.
An adaptive attack is one where the attacker is aware of the defenses and
adapts their strategy accordingly. Our proposed method leverages adversarial
training to reinforce the ability to detect attacks, without compromising clean
accuracy. During the training phase, we integrate into the dataset adversarial
examples, which were optimized to fool both the classifier and the adversarial
detector, enabling the adversarial detector to learn and adapt to potential
attack scenarios. Experimental evaluations on the CIFAR-10 and SVHN datasets
demonstrate that our proposed algorithm significantly improves a detector's
ability to accurately identify adaptive adversarial attacks -- without
sacrificing clean accuracy.