Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay

arXiv:2404.01828v1 Announce Type: new
Abstract: Deep neural networks have demonstrated susceptibility to adversarial attacks. Adversarial defense techniques often focus on one-shot setting to maintain robustness against attack. However, new attacks can emerge in sequences in real-world deployment scenarios. As a result, it is crucial for a defense model to constantly adapt to new attacks, but the adaptation process can lead to catastrophic forgetting of previously defended against attacks. In this paper, we discuss for the first time the concept of …

abstract adversarial adversarial attacks arxiv attacks continual cs.ai cs.lg defense deployment focus however networks neural networks robustness type world