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Benchmark Results – FaceForensics Benchmark via IFTF


via Benchmark Results – FaceForensics Benchmark

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

Method Info Deepfakes Face2Face FaceSwap NeuralTextures Pristine Total
sort by sort by sort by sort by sort by sorted by
Xception permissive 0.964 0.869 0.903 0.807 0.524 0.710
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, Matthias Nießner: FaceForensics++: Learning to Detect Manipulated Facial Images. ICCV 2019
MesoNet 0.873 0.562 0.612 0.407 0.726 0.660
Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen: Mesonet: a compact facial video forgery detection network. arXiv
XceptionNet Full Image permissive 0.745 0.759 0.709 0.733 0.510 0.624
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, Matthias Nießner: FaceForensics++: Learning to Detect Manipulated Facial Images. ICCV 2019
Bayar and Stamm 0.845 0.737 0.825 0.707 0.462 0.616
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
Rahmouni 0.855 0.642 0.563 0.607 0.500 0.581
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,
Recasting 0.855 0.679 0.738 0.780 0.344 0.552
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 Features 0.736 0.737 0.689 0.633 0.340 0.518
Jessica Fridrich and Jan Kodovsky: Rich Models for Steganalysis of Digital Images. IEEE Transactions on Information Forensics and Security

 

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