Abstract Generative Adversarial Networks (GAN) have led to the generation of very realistic
face images, which have been used in fake social media accounts and other disinformation …

Differences in forgery attributes of images generated in CNN-synthesized and image-editing
domains are large, and such differences make a unified image forgery detection and …

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 …

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 …

Over the past two decades, biometric recognition has exploded into a plethora of different
applications around the globe. This proliferation can be attributed to the high levels of …

Image manipulation detection algorithms are often trained to discriminate between images
manipulated with particular Generative Models (GMs) and genuine/real images, yet …

Advancements in the generation quality of various Generative Models (GMs) has made it
necessary to not only perform binary manipulation detection but also localize the modified …

The zero-shot open-vocabulary setting poses challenges for image classification.
Fortunately, utilizing a vision-language model like CLIP, pre-trained on image-textpairs …

Differences in forgery attributes of images generated in CNN-synthesized and image-editing
domains are large, and such differences make a unified image forgery detection and …