Intra-frame inconsistency has been proved to be effective for the generalization of face
forgery detection. However, learning to focus on these inconsistency requires extra pixel …

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 …

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 …

As advanced facial manipulation technologies develop rapidly, one can easily modify an
image by changing the identity or the facial expression of the target person, which threatens …

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 …

Existing face forgery detection models try to discriminate fake images by detecting only
spatial artifacts (eg, generative artifacts, blending) or mainly temporal artifacts (eg, flickering …

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 …

With the rapid development of facial manipulation techniques, face forgery has received
considerable attention in digital media forensics due to security concerns. Most existing …