  Face Anti-Spoofing (FAS) is crucial for securing face recognition systems
against presentation attacks. With advancements 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 challenges arise from (1) modality unreliability, where some
modality sensors like depth and infrared undergo significant 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 convergence of others, reducing
effectiveness against attack types that are indistinguishable 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 modalities. For modality imbalance, we propose a Rebalanced Modality
Gradient Modulation (ReGrad) strategy to rebalance the convergence speed of all
modalities by adaptively adjusting their gradients. Besides, we provide the
first large-scale benchmark for evaluating multi-modal FAS performance under
domain generalization scenarios. Extensive experiments demonstrate that our
method outperforms state-of-the-art methods. Source code and protocols will be
released on https://github.com/OMGGGGG/mmdg.
