Multi-modal face anti-spoofing via self-supervised learning

High-performance Face Anti-Spoofing (FAS) systems depend critically on extensive labeled data, while Multi-Modal FAS (MMFAS) exacerbates this dependency due to the increased complexity of collecting and annotating multi-modal data. To address this challenge, we propose a novel framework called Multi-Modal Self-Supervised FAS (M ² S ² FAS), which introduces a new network architecture alongside three carefully designed pretext tasks. The primary objective of M ² S ² FAS is to model inter-modal correspondences, thereby generating versatile pre-trained weights that can be effectively utilized across different modalities. Our framework demonstrates its efficacy through minimal fine-tuning, achieving high-performance levels. Extensive experimental evaluations on widely used multi-modal FAS datasets validate the superiority of the proposed method.