  Domain adaptation (DA) or domain generalization (DG) for face presentation
attack detection (PAD) has attracted attention recently with its robustness
against unseen attack scenarios. Existing DA/DG-based PAD methods, however,
have not yet fully explored the domain-specific style information that can
provide knowledge regarding attack styles (e.g., materials, background,
illumination and resolution). In this paper, we introduce a novel Style-Guided
Domain Adaptation (SGDA) framework for inference-time adaptive PAD.
Specifically, Style-Selective Normalization (SSN) is proposed to explore the
domain-specific style information within the high-order feature statistics. The
proposed SSN enables the adaptation of the model to the target domain by
reducing the style difference between the target and the source domains.
Moreover, we carefully design Style-Aware Meta-Learning (SAML) to boost the
adaptation ability, which simulates the inference-time adaptation with style
selection process on virtual test domain. In contrast to previous domain
adaptation approaches, our method does not require either additional auxiliary
models (e.g., domain adaptors) or the unlabeled target domain during training,
which makes our method more practical to PAD task. To verify our experiments,
we utilize the public datasets: MSU-MFSD, CASIA-FASD, OULU-NPU and Idiap
REPLAYATTACK. In most assessments, the result demonstrates a notable gap of
performance compared to the conventional DA/DG-based PAD methods.
