Over the last few decades, rapid progress in AI, machine learning, and deep learning has
resulted in new techniques and various tools for manipulating multimedia. Though the …

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

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 …

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 …

Misinformation has become a pressing issue. Fake media, in both visual and textual forms,
is widespread on the web. While various deepfake detection and text fake news detection …

Large-scale datasets have played indispensable roles in the recent success of face
generation/editing and significantly facilitated the advances of emerging research fields …

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 …

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 …