Generative deep learning algorithms have progressed to a point where it is difficult to tell the
difference between what is real and what is fake. In 2018, it was discovered how easy it is to …

When used maliciously, deepfake can pose detrimental implications to political and social
forces including reducing public trust in institutions, damaging the reputation of prominent …

We propose a neural rendering-based system that creates head avatars from a single
photograph. Our approach models a person's appearance by decomposing it into two …

Deep learning based methods have shown dramatic improvements in image rain removal
by using large-scale paired data of synthetic datasets. However, due to the various …

Improving the performance of face forgery detectors often requires more identity-swapped
images of higher-quality. One core objective of identity swap** is to generate identity …

Recently, face manipulation techniques have caused increasing trust concerns in our
society. Although current face manipulation detection methods achieve impressive …

We propose a neural head reenactment system, which is driven by a latent pose
representation and is capable of predicting the foreground segmentation alongside the RGB …

Deep neural networks have achieved impressive performance in a variety of tasks over the
last decade, such as autonomous driving, face recognition, and medical diagnosis …

This paper focuses on the facial micro-expression (FME) generation task, which has
potential application in enlarging digital FME datasets, thereby alleviating the lack of training …

Substantial improvements have been achieved in the field of face recognition due to the
successful application of deep neural networks. However, existing methods are sensitive to …