  Face recognition systems have raised concerns due to their vulnerability to
different presentation attacks, and system security has become an increasingly
critical concern. Although many face anti-spoofing (FAS) methods perform well
in intra-dataset scenarios, their generalization remains a challenge. To
address this issue, some methods adopt domain adversarial training (DAT) to
extract domain-invariant features. However, the competition between the encoder
and the domain discriminator can cause the network to be difficult to train and
converge. In this paper, we propose a domain adversarial attack (DAA) method to
mitigate the training instability problem by adding perturbations to the input
images, which makes them indistinguishable across domains and enables domain
alignment. Moreover, since models trained on limited data and types of attacks
cannot generalize well to unknown attacks, we propose a dual perceptual and
generative knowledge distillation framework for face anti-spoofing that
utilizes pre-trained face-related models containing rich face priors.
Specifically, we adopt two different face-related models as teachers to
transfer knowledge to the target student model. The pre-trained teacher models
are not from the task of face anti-spoofing but from perceptual and generative
tasks, respectively, which implicitly augment the data. By combining both DAA
and dual-teacher knowledge distillation, we develop a dual teacher knowledge
distillation with domain alignment framework (DTDA) for face anti-spoofing. The
advantage of our proposed method has been verified through extensive ablation
studies and comparison with state-of-the-art methods on public datasets across
multiple protocols.
