Face recognition has been widely applied in identity authentication systems, but its vulnerability to various presentation attacks poses significant security risks. As a result, face anti-spoofing (FAS) has become one of the key technologies for ensuring the reliability of such systems. Most existing domain- generalized FAS (DGFAS) approaches rely on domain distribution alignment to learn cross-domain invariant representations. However, many of these methods independently model each sample while overlooking semantic consistency among cross-domain samples and different regions within each sample, making them vulnerable to semantic drift. Moreover, some existing methods introduce additional pixel-level supervision signals, such as pseudo-depth maps and binary masks, which increase annotation costs and limit their generalizability. To address these challenges, we propose DSCM-FAS, a novel framework that combines inter-sample and intra-sample semantic consistency to enhance robustness and cross-domain generalization, without requiring auxiliary supervision. Specifically, we design a Dual Semantic Consistency Module (DSCM). Across samples, contrastive learning is leveraged to learn discriminative representations, followed by the adaptive construction of a high-confidence similarity adjacency graph via statistical thresholding. The resulting graph is then fed into a graph convolutional network (GCN) to strengthen cross-domain semantic consistency, thereby improving the model's robustness to domain shifts. Within each sample, the intermediate embedding features are first partitioned into multiple patches, and Laplacian Regularization is introduced to constrain the semantic relationships among different patches. This effectively suppresses local noise interference and promotes the learning of more robust and semantically consistent feature representations. Extensive experiments on four public FAS datasets demonstrate that DSCM-FAS consistently outperforms state-of-the-art methods under various protocols. These results validate the effectiveness of our DSCM-FAS in improving the cross-domain generalization of FAS models.
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