Abstract
Adversarial example detection has emerged as a prominent defense strategy owing to its efficiency in training and deployment. Nevertheless, existing detectors are typically developed under a single-step paradigm, where models become static after training on adversarial examples generated by a single attack. This paradigm is infeasible in dynamic real-world scenarios, since retraining from scratch for each newly encountered attack is impractical and computationally prohibitive. To address this limitation, we propose Continual Adversarial example Detection via Incremental Attack Configuration (IAC-CAD), which pioneers to exploit continual learning for adversarial detection within a knowledge distillation framework. IAC-CAD constructs a sequence of continuous detection tasks which require only a limited number of samples per task. Moreover, the proposed Incremental Attack Configuration (IAC) mechanism selects the representative attacks which maximally cover the entire adversarial feature space and optimizes their training sequence through the Memory-aware Attack Ordering. This design simultaneously mitigates catastrophic forgetting of known attacks and enhances generalization ability against unseen attacks. Extensive experiments verify the superiority and practicality of IAC-CAD.