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 been a persistent and harmful phenomenon affecting our society in
various ways, including individuals' physical health and economic stability. With the rise of …

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 …

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 …

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 …

Image is a powerful way to share information in the digital world. The sources of images are
everywhere, magazines, newspapers, healthcare, entertainment, education, social media …

In response to the rapid advancements in facial manipulation technologies, particularly
facilitated by Generative Adversarial Networks (GANs) and Stable Diffusion-based methods …

Deepfake attacks, malicious manipulation of media containing people, are a serious
concern for society. Conventional deepfake detection methods train supervised classifiers to …

Existing face forgery detection usually follows the paradigm of training models in a single
domain, which leads to limited generalization capacity when unseen scenarios and …

This paper introduces a novel approach for high-quality deepfake detection called Localized
Artifact Attention Network (LAA-Net). Existing methods for high-quality deepfake detection …