Deepfakes have recently raised significant trust issues and security concerns among the
public. Compared to CNN face forgery detectors, ViT-based methods take advantage of the …

Though existing methods for face forgery detection achieve fairly good performance under
the intra-dataset scenario, few of them gain satisfying results in the case of cross-dataset …

With the development of deepfake technology, deepfake detection has received widespread
attention. Although some deepfake forensics techniques have been proposed, they are still …

Currently, many face forgery detection methods aggregate spatial and frequency features to
enhance the generalization ability and gain promising performance under the cross-dataset …

Manipulated images and videos, ie, deepfakes have become increasingly realistic due to
the tremendous progress of deep learning methods. However, such manipulation has …

Deepfake videos and images are becoming increasingly credible, posing a significant threat
given their potential to facilitate fraud or bypass access control systems. This has motivated …

Deepfake videos are becoming increasingly realistic, showing subtle tampering traces on
facial areasthat vary between frames. Consequently, many existing Deepfake detection …

With the rapid and continuous development of AIGC, It is becoming increasingly difficult to
distinguish between real and forged facial images, which calls for efficient forgery detection …

With diverse presentation forgery methods emerging continually, detecting the authenticity of
images has drawn growing attention. Although existing methods have achieved impressive …

Deepfake techniques have been widely used for malicious purposes, prompting extensive
research interest in developing Deepfake detection methods. Deepfake manipulations …