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

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 …

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 …

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

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 …

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 …

State-of-the-art deepfake detectors perform well in identifying forgeries when they are
evaluated on a test set similar to the training set, but struggle to maintain good performance …

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 …

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

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 …