Malicious applications of deepfakes (ie, technologies generating target facial attributes or
entire faces from facial images) have posed a huge threat to individuals' reputation and …

Face forgery detection is essential in combating malicious digital face attacks. Previous
methods mainly rely on prior expert knowledge to capture specific forgery clues, such as …

Recent studies on face forgery detection have shown satisfactory performance for methods
involved in training datasets, but are not ideal enough for unknown domains. This motivates …

Convolutional neural network based face forgery detection methods have achieved
remarkable results during training, but struggled to maintain comparable performance …

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

Recently, Deepfake arises as a powerful tool to fool the existing real-world face detection
systems, which has received wide attention in both academia and society. Most existing …

Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection
methods are typically inadequate in generalizability, with a tendency to overfit to image …

For fake face image detection, most existing detectors exploit local artifacts, ignoring the
mining of structured forgery clues existed in global images, which greatly constrains …

Detecting digital face manipulation has attracted extensive attention due to fake media's
potential risks to the public. However, recent advances have been able to reduce the forgery …

Online media data, in the forms of images and videos, are becoming mainstream
communication channels. However, recent advances in deep learning, particularly deep …