Physical Semantic Graph for Face Anti-Spoofing via Cross-Attribute Consistency Modeling

Recent RGB face anti-spoofing (FAS) methods have achieved remarkable progress by learning discriminative representations from various visual cues. However, most existing approaches remain predominantly feature-driven, focusing on individual cues or their fusion while paying limited attention to the relational structure among heterogeneous physical evidence. In this paper, we introduce a new perspective that characterizes facial liveness through cross-attribute consistency among complementary physical semantics. Specifically, we propose a fixed Physical Semantic Graph (PSG), where each node represents observable or estimable semantic evidence associated with a physical property of facial liveness, including texture, frequency, geometry, reflectance, and physiology-related evidence, while graph edges encode prior relational hypotheses derived from physical dependencies and complementary observations. Based on PSG, we develop a cross-attribute reasoning mechanism that propagates complementary evidence along these predefined relations to learn a relational representation of facial liveness. Furthermore, we introduce Pairwise Physical Consistency Regularization (PPCR), which explicitly encourages the relational patterns of genuine faces to be more consistent than those of presentation attacks. Extensive cross-dataset experiments on multiple public FAS benchmarks demonstrate competitive generalization.