In this work, we propose a novel method to improve the generalization ability of CNN-based
face forgery detectors. Our method considers the feature anomalies of forged faces caused …

Current face forgery detection methods achieve high accuracy under the within-database
scenario where training and testing forgeries are synthesized by the same algorithm …

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 …

Face forgery detection is raising ever-increasing interest in computer vision since facial
manipulation technologies cause serious worries. Though recent works have reached …

With various facial manipulation techniques arising, face forgery detection has drawn
growing attention due to security concerns. Previous works always formulate face forgery …

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

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

Although current deep learning-based face forgery detectors achieve impressive
performance in constrained scenarios, they are vulnerable to samples created by unseen …

Beyond high accuracy, good interpretability is very critical to deploy a face forgery detection
model for visual content analysis. In this paper, we propose learning patch-channel …

In this paper, we propose a domain-general model, termed learning-to-weight (LTW), that
guarantees face detection performance across multiple domains, particularly the target …