Robust Multi-Modal Face Anti-Spoofing with Domain Adaptation: Tackling Missing Modalities, Noisy Pseudo-Labels, and Model Degradation

Recent multi-modal face anti-spoofing (FAS) methods have investigated the potential of leveraging multiple modalities to distinguish live and spoof faces. However, pre-adapted multi-modal FAS models often fail to detect unseen attacks from new target domains. Although a more realistic domain adaptation (DA) scenario has been proposed for single-modal FAS to learn specific spoof attacks during inference, DA remains unexplored in multi-modal FAS methods. In this paper, we propose a novel framework, MFAS-DANet, to address three major challenges in multi-modal FAS under the DA scenario: missing modalities, noisy pseudo labels, and model degradation. First, to tackle the issue of missing modalities, we propose extracting complementary features from other modalities to substitute missing modality features or enhance existing ones. Next, to reduce the impact of noisy pseudo labels during model adaptation, we propose deriving reliable pseudo labels by leveraging prediction uncertainty across different modalities. Finally, to prevent model degradation, we design an adaptive mechanism that decreases the loss weight during unstable adaptations and increasing it during stable ones. Extensive experiments demonstrate the effectiveness and state-of-the-art performance of our proposed MFAS-DANet.