Benchmark Results - FaceForensics Benchmark

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

Method	Deepfakes	Face2Face	FaceSwap	NeuralTextures	Pristine	Total

DirechletEnsemble-Classifier	0.955	0.956	0.932	0.967	0.992	0.973

SRTNet_formal_v2	0.982	0.956	0.951	0.960	0.066	0.514

DFirt	0.982	0.942	0.961	0.893	0.494	0.717

Siam-ensemble	0.945	0.934	0.951	0.747	0.748	0.816

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

EffB7_DataAug_v7	0.964	0.920	0.951	0.867	0.710	0.815

ZAntiFakeBio	1.000	0.920	0.971	0.907	0.936	0.940

Beijing ZKJ	1.000	0.920	0.961	0.880	0.948	0.941

EnbFW	0.973	0.912	0.951	0.727	0.878	0.878

eff-b7-v3	0.973	0.912	0.913	0.807	0.198	0.546

Sentinel	0.964	0.905	0.883	0.867	0.624	0.763

NoSenseAtAll	0.982	0.905	0.951	0.827	0.908	0.908

EfficienNetb7	0.973	0.905	0.922	0.720	0.868	0.868

MixingExpert	1.000	0.905	0.942	0.780	0.922	0.909

yjzenb_lowqulity	0.973	0.905	0.932	0.727	0.880	0.876

en7_yjz	0.973	0.898	0.922	0.720	0.880	0.873

RobustForensics	0.991	0.891	0.951	0.807	0.904	0.902

ENbsoftFW1111	0.982	0.891	0.922	0.787	0.898	0.892

lcy	0.964	0.883	0.893	0.573	0.890	0.850

EfficientB5_OHEM	0.918	0.876	0.913	0.653	0.848	0.837

Yjz_jerry	0.973	0.876	0.893	0.713	0.840	0.846

shangci	0.973	0.876	0.942	0.747	0.854	0.863

face single model	0.964	0.869	0.942	0.460	0.738	0.760

simple policy	0.955	0.869	0.942	0.387	0.744	0.751

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
Balance	0.918	0.861	0.883	0.733	0.876	0.858

Leo	1.000	0.861	0.971	0.853	0.922	0.917

yd dust face	0.955	0.861	0.951	0.693	0.762	0.806

Aquarius	1.000	0.854	0.971	0.807	0.884	0.890

faceClassify1	0.945	0.854	0.981	0.793	0.730	0.806

EffB7_DataAug_v1	0.945	0.847	0.825	0.813	0.712	0.783

UltraVision	0.918	0.847	0.932	0.733	0.810	0.828

EfficientNetB7	0.836	0.847	0.874	0.747	0.630	0.725

IR+unet	0.909	0.847	0.845	0.787	0.790	0.816

result_all_best_th0.85_517_ens	0.945	0.847	0.854	0.740	0.866	0.852

StableForensics	0.991	0.847	0.951	0.787	0.884	0.883

PredictFake	0.973	0.847	0.913	0.820	0.894	0.887

LGSC_Lite	0.955	0.839	0.874	0.773	0.844	0.848

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
FAKEDET	0.964	0.832	0.922	0.687	0.918	0.877

unet+res	0.882	0.832	0.796	0.767	0.812	0.814

focbe	0.900	0.832	0.786	0.793	0.828	0.827

YJZ_419_513_ensemble	0.964	0.832	0.951	0.780	0.808	0.839

Xceptio InceptionRes Efficient	0.964	0.825	0.864	0.827	0.768	0.816

b7_514_e2_luo_yang_efficienet	0.936	0.825	0.854	0.700	0.884	0.851

effb7_newdataaug_v7_aligne	0.973	0.825	0.961	0.660	0.804	0.820

SCAN	0.909	0.825	0.738	0.613	0.764	0.763

MMIG-Net	0.909	0.825	0.816	0.887	0.566	0.713

AllDataRes_EB5	0.964	0.818	0.845	0.647	0.900	0.852

696_723_yjz	0.964	0.818	0.893	0.740	0.878	0.860

IR-Capsule	0.973	0.818	0.942	0.793	0.792	0.831

effb7_newdataaug_v7_aligne_v2	0.945	0.810	0.942	0.613	0.884	0.846

YJZ_ensemmble	0.936	0.810	0.942	0.753	0.794	0.821

CONG	0.955	0.810	0.854	0.780	0.874	0.858

antifake	0.936	0.803	0.942	0.687	0.764	0.795

EfficientNet-b7-Attention	0.955	0.803	0.854	0.600	0.616	0.701

gg2	0.327	0.803	0.117	0.740	0.908	0.723

jiayoua	0.327	0.803	0.117	0.740	0.908	0.723

EfficientNet-b4	0.955	0.796	0.825	0.827	0.712	0.779

ATDETECTOR	0.955	0.796	0.922	0.780	0.898	0.875

InTeLe_	1.000	0.796	0.922	0.720	0.884	0.864

iouiwc-31	0.964	0.796	0.893	0.760	0.848	0.845

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

Cancer	0.964	0.781	0.942	0.780	0.952	0.903

xceptattention	0.791	0.774	0.456	0.747	0.680	0.692

KBNet	0.909	0.774	0.854	0.793	0.852	0.839

RealFace	0.945	0.766	0.864	0.813	0.790	0.815

fakeface	0.955	0.766	0.883	0.747	0.806	0.816

DFSpot-Ensemble	0.900	0.766	0.825	0.700	0.750	0.769

swap_classify	0.909	0.759	0.864	0.747	0.724	0.767

b7_e2_ensemble_chun	0.927	0.759	0.806	0.687	0.900	0.842

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
LVLNet	0.964	0.752	0.796	0.687	0.694	0.741

yangjiezhi-ensemble	0.927	0.745	0.816	0.700	0.884	0.835

winer_ensemble_correct2	0.873	0.745	0.816	0.560	0.896	0.814

framelevel	0.873	0.737	0.835	0.760	0.760	0.777

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
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
ZFake	0.945	0.737	0.864	0.693	0.880	0.838

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
EfficientNet-v0	0.827	0.730	0.806	0.533	0.420	0.564

Firefly	0.955	0.730	0.874	0.667	0.920	0.855

AttentionExcep	0.782	0.723	0.524	0.747	0.700	0.701

SiameseDF	0.864	0.723	0.709	0.667	0.750	0.742

EfficientB4_380	0.882	0.701	0.796	0.740	0.794	0.783

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
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
Yjz	0.964	0.664	0.942	0.553	0.920	0.837

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,
Forged face detection : fCNN	0.791	0.642	0.709	0.513	0.544	0.597
Aditi Kohli, Abhinav Gupta: Detecting DeepFake, FaceSwap and Face2Face facial forgeries using frequency CNN. Multimedia Tools and Application, Springer
PPPP	0.827	0.635	0.621	0.480	0.628	0.628

Yolo Object detection	0.864	0.628	0.757	0.667	0.854	0.786

HRC	0.827	0.613	0.573	0.660	0.848	0.757

HKU_EfficientNet-B7_v2	0.845	0.606	0.806	0.447	0.864	0.758

Yolo with Voting Mechanism	0.855	0.606	0.709	0.587	0.916	0.796

YSNet	0.564	0.584	0.592	0.427	0.492	0.513

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
FullEfficientNet	0.836	0.526	0.728	0.480	0.880	0.751

resnet50	0.527	0.504	0.408	0.387	0.786	0.620

Xception_ForgeryNet	0.818	0.496	0.544	0.407	0.632	0.591

IAMFAKE2	0.464	0.496	0.515	0.367	0.826	0.640

EfficientNetTest	0.709	0.445	0.631	0.387	0.674	0.599

IAMFAKE	0.436	0.409	0.524	0.300	0.816	0.611

EffB8_FFDFDCDFCelebAISG	0.382	0.409	0.447	0.433	0.660	0.539

inceptionv1	0.655	0.350	0.544	0.333	0.768	0.610

YjzV4	0.200	0.153	0.107	0.707	0.938	0.629

framelevel_all_compress_format	0.782	0.124	0.155	0.173	0.860	0.575

lcy2	0.900	0.073	0.845	0.053	0.950	0.679

Top8-soft-withoutnt	0.300	0.066	0.913	0.007	0.974	0.624