This table lists the benchmark results for the Binary Classification scenario.


Method InfoDeepfakesFace2FaceFaceSwapNeuralTexturesPristineTotal
sort bysort bysort bysort bysort bysorted by
swap_classify0.9090.7590.8640.7470.7240.767
SCANcopyleft0.9640.8830.8060.8000.6060.733
Xceptionpermissive0.9640.8690.9030.8070.5240.710
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, Matthias Nießner: FaceForensics++: Learning to Detect Manipulated Facial Images. ICCV 2019
MesoNet0.8730.5620.6120.4070.7260.660
Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen: Mesonet: a compact facial video forgery detection network. arXiv
XceptionNet Full Imagepermissive0.7450.7590.7090.7330.5100.624
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, Matthias Nießner: FaceForensics++: Learning to Detect Manipulated Facial Images. ICCV 2019
Bayar and Stamm0.8450.7370.8250.7070.4620.616
Belhassen Bayar and Matthew C. Stamm: A deep learning approach to universal image manipulation detection using a new convolutional layer. ACM Workshop on Information Hiding and Multimedia Security
Rahmouni0.8550.6420.5630.6070.5000.581
Nicolas Rahmouni, Vincent Nozick, Junichi Yamagishi, and Isao Echizen: Distinguishing computer graphics from natural images using convolution neural networks. IEEE Workshop on Information Forensics and Security,
Recasting0.8550.6790.7380.7800.3440.552
Davide Cozzolino, Giovanni Poggi, and Luisa Verdoliva: Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection. ACM Workshop on Information Hiding and Multimedia Security
Steganalysis Features0.7360.7370.6890.6330.3400.518
Jessica Fridrich and Jan Kodovsky: Rich Models for Steganalysis of Digital Images. IEEE Transactions on Information Forensics and Security