Over the last few decades, rapid progress in AI, machine learning, and deep learning has
resulted in new techniques and various tools for manipulating multimedia. Though the …

Although current face manipulation techniques achieve impressive performance regarding
quality and controllability, they are struggling to generate temporal coherent face videos. In …

One of the most pressing challenges for the detection of face-manipulated videos is
generalising to forgery methods not seen during training while remaining effective under …

The creation or manipulation of facial appearance through deep generative approaches,
known as DeepFake, have achieved significant progress and promoted a wide range of …

Big data analytics, computer vision, and human-level governance are key areas where deep
learning has been impactful. However, its advancements have also led to concerns over …

DeepFakes detection approaches have to be agnostic across generation type, quality, and
appearance to provide a generalizable DeepFakes detector. Limited generalizability will …

Recently, realistic DeepFake videos have raised severe security concerns in society.
Existing video-based detection methods observe local spatial regions with the coarse …

With the continuous development of deep learning in the field of image generation models, a
large number of vivid forged faces have been generated and spread on the Internet. These …

Recently, DeepFake videos have developed rapidly, causing new security issues in society.
Due to the rough spatiotemporal view, existing video-based detection methods struggle to …

The rapid development of DeepFake technologies has brought great challenges to the
authenticity of video contents. It is of vital importance to develop DeepFake detection …