Results 11 to 20 of about 130,964 (204)
Learning to Discover Forgery Cues for Face Forgery Detection
IEEE Transactions on Information Forensics and SecurityTIFS ...
Jiahe Tian +6 more
exaly +5 more sources
Local Relation Learning for Face Forgery Detection [PDF]
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 With the rapid development of facial manipulation techniques, face forgery has received considerable attention in digital media forensics due to security concerns. Most existing methods formulate face forgery detection as a classification problem and utilize binary labels or manipulated region masks as supervision.
Chen, Shen +5 more
openaire +3 more sources
FedForgery: Generalized Face Forgery Detection With Residual Federated Learning
IEEE Transactions on Information Forensics and Security, 2023 With the continuous development of deep learning in the field of image generation models, a large number of vivid forged faces have been generated and spread on the Internet. These high-authenticity artifacts could grow into a threat to society security.
Decheng Liu +6 more
openaire +4 more sources
Wavelet-Based Texture Mining and Enhancement for Face Forgery Detection
IET BiometricsDue to the abuse of deep forgery technology, the research on forgery detection methods has become increasingly urgent. The corresponding relationship between the frequency spectrum information and the spatial clues, which is often neglected by current ...
Xin Li +3 more
doaj +2 more sources
Generalizing Face Forgery Detection with High-frequency Features [PDF]
Computer Vision and Pattern Recognition, 2021 Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario
Yucheng Luo +3 more
semanticscholar +1 more source
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain [PDF]
Computer Vision and Pattern Recognition, 2021 The remarkable success in face forgery techniques has received considerable attention in computer vision due to security concerns. We observe that up-sampling is a necessary step of most face forgery techniques, and cumulative up-sampling will result in ...
Honggu Liu +7 more
semanticscholar +1 more source
Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection
International Conference on Learning RepresentationsComment: ICLR 2024 ...
Liang, Jiawei +5 more
openaire +3 more sources
Exploring Temporal Coherence for More General Video Face Forgery Detection [PDF]
IEEE International Conference on Computer Vision, 2021 Although current face manipulation techniques achieve impressive performance regarding quality and controllability, they are struggling to generate temporal coherent face videos.
Yinglin Zheng +4 more
semanticscholar +1 more source
AltFreezing for More General Video Face Forgery Detection [PDF]
Computer Vision and Pattern Recognition, 2023 Existing face forgery detection models try to discriminate fake images by detecting only spatial artifacts (e.g., generative artifacts, blending) or mainly temporal artifacts (e.g., flickering, discontinuity).
Zhendong Wang +4 more
semanticscholar +1 more source
Dual Contrastive Learning for General Face Forgery Detection [PDF]
AAAI Conference on Artificial Intelligence, 2021 With various facial manipulation techniques arising, face forgery detection has drawn growing attention due to security concerns. Previous works always formulate face forgery detection as a classification problem based on cross-entropy loss, which ...
Ke Sun +5 more
semanticscholar +1 more source