Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

Deep Learning (DL) is the most efficient technique to handle a wide range of challenging
problems such as data analytics, diagnosing diseases, detecting anomalies, etc. The …

With diverse presentation attacks emerging continually, generalizable face anti-spoofing
(FAS) has drawn growing attention. Most existing methods implement domain generalization …

Face anti-spoofing (FAS) plays a vital role in face recognition systems. Most state-of-the-art
FAS methods 1) rely on stacked convolutions and expert-designed network, which is weak …

Deep learning has been successfully applied to solve various complex problems ranging
from big data analytics to computer vision and human-level control. Deep learning advances …

This work studies the generalization issue of face anti-spoofing (FAS) models on domain
gaps, such as image resolution, blurriness and sensor variations. Most prior works regard …

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in
securing face recognition systems from presentation attacks (PAs). As more and more …

Face anti-spoofing (FAS) plays a critical role in securing face recognition systems from
different presentation attacks. Previous works leverage auxiliary pixel-level supervision and …

Recent advances in media generation techniques have made it easier for attackers to create
forged images and videos. State-of-the-art methods enable the real-time creation of a forged …

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Existing
methods heavily rely on the expert-designed networks, which may lead to a sub-optimal …