Abstract Generative Adversarial Networks (GAN) have led to the generation of very realistic
face images, which have been used in fake social media accounts and other disinformation …

Manipulated videos often contain subtle inconsistencies between their visual and audio
signals. We propose a video forensics method, based on anomaly detection, that can …

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 …

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

Face forgery detection is essential in combating malicious digital face attacks. Previous
methods mainly rely on prior expert knowledge to capture specific forgery clues, such as …

Recent studies on face forgery detection have shown satisfactory performance for methods
involved in training datasets, but are not ideal enough for unknown domains. This motivates …

Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection
methods are typically inadequate in generalizability, with a tendency to overfit to image …

This work explores various ways of exploring multi-task learning (MTL) techniques aimed at
classifying videos as original or manipulated in cross-manipulation scenario to attend …

The malicious use and widespread dissemination of deepfake pose a significant crisis of
trust. Current deepfake detection models can generally recognize forgery images by training …

Modern deepfake detectors have achieved encouraging results, when training and test
images are drawn from the same data collection. However, when these detectors are …