Adversarially Robust Continual Learning with Anti-Forgetting Loss

Existing continual learning methods focus on preventing catastrophic forgetting but often overlook the challenge of adversarial examples in image classification. In this study, we propose a novel method that balances accuracy, robustness against adversarial examples, and the prevention of forgetting. Specifically, we first theoretically and experimentally demonstrate that learning through knowledge distillation, a common strategy in continual learning, conflicts with learning through the cross-entropy loss. To resolve this conflict, we propose a novel loss function that combines an additional memory data loss with a conflict-avoiding knowledge distillation loss, effectively preventing catastrophic forgetting while ensuring robustness. Experimental results show that the proposed method outperforms existing methods by 5.17% in clean accuracy and $2.10 \%$ in robust accuracy. This method proves to be especially beneficial in scenarios where the reuse of samples from previous tasks is limited.