Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing

Abstract

Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against presentation attacks. With ad-vancements in sensor manufacture and multi-modal learning techniques, many multi-modal FAS approaches have emerged. However, they face challenges in generalizing to unseen attacks and deployment conditions. These chal-lenges arise from (1) modality unreliability, where some modality sensors like depth and infrared undergo signifi-cant domain shifts in varying environments, leading to the spread of unreliable information during cross-modal feature fusion, and (2) modality imbalance, where training overly relies on a dominant modality hinders the conver-gence of others, reducing effectiveness against attack types that are indistinguishable by sorely using the dominant modality. To address modality unreliability, we propose the Uncertainty-Guided Cross-Adapter (U-Adapter) to recognize unreliably detected regions within each modality and suppress the impact of unreliable regions on other modal-ities. For modality imbalance, we propose a Rebalanced Modality Gradient Modulation (ReGrad) strategy to rebal-ance the convergence speed of all modalities by adaptively adjusting their gradients. Besides, we provide the first large-scale benchmark for evaluating multi-modal FAS per-formance under domain generalization scenarios. Exten-sive experiments demonstrate that our method outperforms state-of-the-art methods. Source codes and protocols are released on https://github.com/OMGGGGG/mmdg.