It is necessary to develop effective face forgery detection methods with constantly evolving
technologies in synthesizing realistic faces which raises serious risks on malicious face …

The deep learning-based face forgery detection is a novel yet challenging task. Despite
impressive results have been achieved, there are still some limitations in the existing …

The rapid advancements in computer vision have stimulated remarkable progress in face
forgery techniques, capturing the dedicated attention of researchers committed to detecting …

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 …

Existing face forgery detection usually follows the paradigm of training models in a single
domain, which leads to limited generalization capacity when unseen scenarios and …

With the rapid growth of Internet technology, security concerns have risen, particularly with
the prevalence of Deepfakes, a popular visual forgery technique. Therefore, there is …

Face forgery generating algorithms that produce a range of manipulated videos/images
have developed quickly. Consequently, this causes an increase in the production of fake …

With the rapid development of face manipulation technology, it is difficult for human eyes to
distinguish fake face images. On the contrary, Convolutional Neural Network (CNN) …

In this paper, we propose a domain-general model, termed learning-to-weight (LTW), that
guarantees face detection performance across multiple domains, particularly the target …

Facial forgery by DeepFake has recently attracted more public attention. Face image
contains sensitive personal information, abuse of such technology will grow into a menace …