  In response to the rapidly evolving nature of adversarial attacks against
visual classifiers on a monthly basis, numerous defenses have been proposed to
generalize against as many known attacks as possible. However, designing a
defense method that generalizes to all types of attacks is not realistic
because the environment in which defense systems operate is dynamic and
comprises various unique attacks that emerge as time goes on. The defense
system must gather online few-shot defense feedback to promptly enhance itself,
leveraging efficient memory utilization. Therefore, we propose the first
continual adversarial defense (CAD) framework that adapts to any attacks in a
dynamic scenario, where various attacks emerge stage by stage. In practice, CAD
is modeled under four principles: (1) continual adaptation to new attacks
without catastrophic forgetting, (2) few-shot adaptation, (3) memory-efficient
adaptation, and (4) high accuracy on both clean and adversarial images. We
explore and integrate cutting-edge continual learning, few-shot learning, and
ensemble learning techniques to qualify the principles. Experiments conducted
on CIFAR-10 and ImageNet-100 validate the effectiveness of our approach against
multiple stages of modern adversarial attacks and demonstrate significant
improvements over numerous baseline methods. In particular, CAD is capable of
quickly adapting with minimal feedback and a low cost of defense failure, while
maintaining good performance against previous attacks. Our research sheds light
on a brand-new paradigm for continual defense adaptation against dynamic and
evolving attacks.
