Benchmark Results - FaceForensics Benchmark

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

Method	Deepfakes	Face2Face	FaceSwap	NeuralTextures	Pristine	Total

PredictFake	0.973	0.847	0.913	0.820	0.894	0.887

StableForensics	0.991	0.847	0.951	0.787	0.884	0.883

ATDETECTOR	0.955	0.796	0.922	0.780	0.898	0.875

RobustForensics	0.973	0.832	0.942	0.760	0.854	0.859

Firefly	0.955	0.730	0.874	0.667	0.920	0.855

LGSC_Lite	0.955	0.839	0.874	0.773	0.844	0.848

ZFake	0.945	0.737	0.864	0.693	0.880	0.838

UltraVision	0.918	0.847	0.932	0.733	0.810	0.828

IR+unet	0.909	0.847	0.845	0.787	0.790	0.816

fakeface	0.955	0.766	0.883	0.747	0.806	0.816

RealFace	0.945	0.766	0.864	0.813	0.790	0.815

unet+res	0.882	0.832	0.796	0.767	0.812	0.814

Inception Resnet V1	0.936	0.839	0.903	0.820	0.750	0.809
Nika Dogonadze, Jana Obernosterer: Deep Face Forgery Detection. Advanced Deep Learning for Computer Vision Course at TUM
faceClassify1	0.945	0.854	0.981	0.793	0.730	0.806

yd dust face	0.955	0.861	0.951	0.693	0.762	0.806

antifake	0.936	0.803	0.942	0.687	0.764	0.795

EfficientB4_380	0.882	0.701	0.796	0.740	0.794	0.783

EfficientNet-b4	0.955	0.796	0.825	0.827	0.712	0.779

framelevel	0.873	0.737	0.835	0.760	0.760	0.777

swap_classify	0.909	0.759	0.864	0.747	0.724	0.767

Sentinel	0.964	0.905	0.883	0.867	0.624	0.763

SCAN	0.909	0.825	0.738	0.613	0.764	0.763

face single model	0.964	0.869	0.942	0.460	0.738	0.760

HRC	0.827	0.613	0.573	0.660	0.848	0.757

simple policy	0.955	0.869	0.942	0.387	0.744	0.751

EfficientNetB7	0.836	0.847	0.874	0.747	0.630	0.725

LVLNet	0.864	0.526	0.699	0.247	0.882	0.717

Xception	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
EfficientNet-b7-Attention	0.955	0.803	0.854	0.600	0.616	0.701

eff-b7-att epc11	0.955	0.788	0.864	0.547	0.592	0.680

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
IAMFAKE2	0.464	0.496	0.515	0.367	0.826	0.640

GAEL-Net	0.718	0.686	0.631	0.707	0.562	0.625
Jae-Yong Baek, Yong-Sang Yoo, Seung-Hwan Bae: Generative Adversarial Ensemble Learning for Face Forensics. IEEE Access
XceptionNet Full Image	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
resnet50	0.527	0.504	0.408	0.387	0.786	0.620

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
IAMFAKE	0.436	0.409	0.524	0.300	0.816	0.611

Forged face detection : fCNN	0.791	0.642	0.709	0.513	0.544	0.597

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,
framelevel_all_compress_format	0.782	0.124	0.155	0.173	0.860	0.575

InceptResV1	0.827	0.730	0.806	0.533	0.420	0.564

inceptionv1	0.827	0.730	0.806	0.533	0.420	0.564

Two-stream-SRM-RGB1	0.982	0.927	0.942	0.953	0.174	0.562

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
eff-b7-v3	0.973	0.912	0.913	0.807	0.198	0.546

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
YSNet	0.564	0.584	0.592	0.427	0.492	0.513