Rehearsal-Free and Efficient Continual Learning for Cross-Domain Face Anti-Spoofing

Face Anti-Spoofing (FAS) is constantly challenged by new attack types and mediums, and thus it is crucial for a FAS model to not only mitigate Catastrophic Forgetting (CF) of previously learned spoofing knowledge on the training data during continual learning but also enhance the model's generalization ability to potential spoofing attacks. In this paper, we first highlight that current strategies for catastrophic forgetting are not well-suited to the imperceptible nature of spoofing information in FAS and lack the focus on improving generalization capability. Then, the instance-wise dynamic central difference convolutional adapter module with the weighted ensemble strategy for Vision Transformer (ViT) is proposed for efficiently fine-tuning with low-shot data by extracting generalized spoofing texture information. Furthermore, we find that catastrophic forgetting in FAS can be reflected through the inconsistent attention matrices of ViT between different continual sessions, as the attention matrices embody relationships of spoofing clues between different patch tokens. Hence, we introduce attention consistency regularization by learning and reusing attention matrices to alleviate catastrophic forgetting. Finally, we devise new protocols and conduct extensive experiments to validate the superior performance of alleviating catastrophic forgetting and generalization on unseen domains.