Public concerns about deepfake face forgery are continually rising in recent years. Most
deepfake detection approaches attempt to learn discriminative features between real and …

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 …

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 …

The fast evolution and widespread of deepfake techniques in real-world scenarios require
stronger generalization abilities of face forgery detectors. Some works capture the features …

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 …

Face forgery by deepfake is widely spread over the internet and has raised severe societal
concerns. Recently, how to detect such forgery contents has become a hot research topic …

Advancements in computer vision and deep learning have led to difficulty in distinguishing
the generated Deepfake media. In addition, recent forgery techniques also modify the audio …

Deepfake technology has given rise to a spectrum of novel and compelling applications.
Unfortunately, the widespread proliferation of high-fidelity fake videos has led to pervasive …

Due to the development of facial manipulation technologies, the generated deepfake videos
cause a severe trust crisis in society. Existing methods prove that effective extraction of the …

The widespread dissemination of facial forgery technology has brought many ethical issues
and aroused widespread concern in society. Most research today treats deepfake detection …