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

In this paper, we consider the face swap** detection from the perspective of face identity.
Face swap** aims to replace the target face with the source face and generate the fake …

Deepfake detection remains a challenging task due to the difficulty of generalizing to new
types of forgeries. This problem primarily stems from the overfitting of existing detection …

Existing face forgery detectors mainly focus on specific forgery patterns like noise
characteristics, local textures, or frequency statistics for forgery detection. This causes …

Recently the proliferation of highly realistic synthetic images facilitated through a variety of
GANs and Diffusions has significantly heightened the susceptibility to misuse. While the …

Recent studies in deepfake detection have yielded promising results when the training and
testing face forgeries are from the same dataset. However, the problem remains challenging …

Deepfake detection faces a critical generalization hurdle with performance deteriorating
when there is a mismatch between the distributions of training and testing data. A broadly …

The growing threats of deepfakes to society and cybersecurity have raised enormous public
concerns, and increasing efforts have been devoted to this critical topic of deepfake video …

In recent years, face forgery detectors have aroused great interest and achieved impressive
performance, but they are still struggling with generalization and robustness. In this work, we …

Face forgery detection becomes increasingly crucial due to the serious security issues
caused by face manipulation techniques. Recent studies in deepfake detection have yielded …