Benchmark Results - FaceForensics Benchmark

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

Method	Deepfakes	Face2Face	FaceSwap	NeuralTextures	Pristine	Total

ZAntiFakeBio	1.000	0.920	0.971	0.907	0.936	0.940

Leo	1.000	0.861	0.971	0.853	0.922	0.917

NoSenseAtAll	0.982	0.905	0.951	0.827	0.908	0.908

Cancer	0.964	0.781	0.942	0.780	0.952	0.903

RobustForensics	0.991	0.891	0.951	0.807	0.904	0.902

Aquarius	1.000	0.854	0.971	0.807	0.884	0.890

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

FAKEDET	0.964	0.832	0.922	0.687	0.918	0.877

ATDETECTOR	0.955	0.796	0.922	0.780	0.898	0.875

Balance	0.918	0.861	0.883	0.733	0.876	0.858

CONG	0.955	0.810	0.854	0.780	0.874	0.858

Firefly	0.955	0.730	0.874	0.667	0.920	0.855

AllDataRes_EB5	0.964	0.818	0.845	0.647	0.900	0.852

LGSC_Lite	0.955	0.839	0.874	0.773	0.844	0.848

KBNet	0.909	0.774	0.854	0.793	0.852	0.839

ZFake	0.945	0.737	0.864	0.693	0.880	0.838

EfficientB5_OHEM	0.918	0.876	0.913	0.653	0.848	0.837

UltraVision	0.918	0.847	0.932	0.733	0.810	0.828

focbe	0.900	0.832	0.786	0.793	0.828	0.827

Xceptio InceptionRes Efficient	0.964	0.825	0.864	0.827	0.768	0.816

fakeface	0.955	0.766	0.883	0.747	0.806	0.816

IR+unet	0.909	0.847	0.845	0.787	0.790	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

HKU_EfficientNet-B7_v2	0.845	0.606	0.806	0.447	0.864	0.758

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

LVLNet	0.964	0.752	0.796	0.687	0.694	0.741

EfficientNetB7	0.836	0.847	0.874	0.747	0.630	0.725

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

PPPP	0.827	0.635	0.621	0.480	0.628	0.628

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

p-DARTS, generalized-cells	0.791	0.730	0.816	0.720	0.478	0.618
Jordi Moreno: Progressive Differentiable Architecture Search for DeepFake Detection. MSc Research Project, from Upf
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

inceptionv1	0.655	0.350	0.544	0.333	0.768	0.610

EfficientNetTest	0.709	0.445	0.631	0.387	0.674	0.599

Forged face detection : fCNN	0.791	0.642	0.709	0.513	0.544	0.597

FullEfficientNet	0.564	0.241	0.427	0.273	0.834	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

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