MuST-GAN MFAS: Multi-semantic spoof tracer GAN with transformer layers for multi-modal face anti-spoofing

Abstract In the field of multi-modal face anti-spoofing (MFAS), where RGB, depth, and infrared data are integrated, remarkable advancements have been seen. However, despite the advancement, there still exist challenges when it comes to adaptability, particularly in dealing with unseen attacks. In this paper, a novel model called MuST-GAN MFAS is presented. This model employs a generative network that incorporates modality-specific encoders and transformer layers. It is significant that the model efficiently disentangles multi-semantic spoof traces by utilizing the power of cross-modal attention mechanisms and a transformer-based spoof trace generator. The training process involves bidirectional adversarial learning, ensuring identity consistency, intensity, center, and classification losses are taken into consideration. Through precise evaluations, it has been shown that the proposed model surpasses existing frameworks, showing remarkable performance when evaluating several modal samples. In the end, MuST-GAN MFAS makes an impressive contribution to the field of face anti-spoofing by offering results that are easy to interpret and emphasizing how important it is to learn multi-semantic spoof traces in order to improve generalization and adaptability to unseen attacks. The code is available at https://github.com/ZainUlAbideenMalik/Must-GAN-MFAS.