Recent years have seen a substantial increase in interest in deepfakes, a fast-develo**
field at the nexus of artificial intelligence and multimedia. These artificial media creations …

We present AvatarReX, a new method for learning NeRF-based full-body avatars from video
data. The learnt avatar not only provides expressive control of the body, hands and the face …

In this paper, we analyse the generalization ability of binary classifiers for the task of
deepfake detection. We find that the stumbling block to their generalization is caused by the …

Existing face forgery detection models try to discriminate fake images by detecting only
spatial artifacts (eg, generative artifacts, blending) or mainly temporal artifacts (eg, flickering …

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 …

This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based
architecture for talking portrait synthesis that can concurrently achieve fast convergence, real …

State-of-the-art deepfake detectors perform well in identifying forgeries when they are
evaluated on a test set similar to the training set, but struggle to maintain good performance …

DeepFake detection aims to differentiate falsified faces from real ones. Most approaches
formulate it as a binary classification problem by solely mining the local artifacts and …

The growing threats of deepfakes to society and cybersecurity have raised enormous public
concerns, and increasing efforts have been devoted to this critical topic of deepfake video …

Face forgery detection becomes increasingly crucial due to the serious security issues
caused by face manipulation techniques. Recent studies in deepfake detection have yielded …