Abstract Face Anti-Spoofing (FAS) is crucial to safeguard Face Recognition (FR) Systems. In
real-world scenarios FRs are confronted with both physical and digital attacks. However …

Abstract Domain generalization (DG) based Face Anti-Spoofing (FAS) aims to improve the
model's performance on unseen domains. Existing methods either rely on domain labels to …

In this paper we study the problem of generalizable synthetic image detection aiming to
detect forgery images from diverse generative methods eg GANs and diffusion models …

Abstract Face Anti-Spoofing (FAS) is pivotal in safeguarding facial recognition systems
against presentation attacks. While domain generalization (DG) methods have been …

Recently, vision transformer (ViT) based multimodal learning methods have been proposed
to improve the robustness of face anti-spoofing (FAS) systems. However, there are still no …

Face Recognition (FR) systems can suffer from physical (ie, print photo) and digital (ie,
DeepFake) attacks. However, previous related work rarely considers both situations at the …

Abstract Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against
presentation attacks. With advancements in sensor manufacture and multi-modal learning …

The generalizable face anti-spoofing (FAS) has attracted much attention recently. Even
though many existing methods perform well under intra-domain settings, the model's …

Deep neural networks are increasingly employed to create adversarial face images, aiming
to deceive face recognition systems. While the majority of studies concentrate on digital …

Multimodal face anti-spoofing systems adopt multiple sensor modalities, such as infrared,
color, depth, and thermal, to distinguish between living and spoofing faces via …