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

Top8-soft-withoutnt	0.300	0.066	0.913	0.007	0.974	0.624

Cancer	0.964	0.781	0.942	0.780	0.952	0.903

lcy2	0.900	0.073	0.845	0.053	0.950	0.679

Beijing ZKJ	1.000	0.920	0.961	0.880	0.948	0.941

YjzV4	0.200	0.153	0.107	0.707	0.938	0.629

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

MixingExpert	1.000	0.905	0.942	0.780	0.922	0.909

Firefly	0.955	0.730	0.874	0.667	0.920	0.855

Yjz	0.964	0.664	0.942	0.553	0.920	0.837

FAKEDET	0.964	0.832	0.922	0.687	0.918	0.877

Yolo with Voting Mechanism	0.855	0.606	0.709	0.587	0.916	0.796

jiayoua	0.327	0.803	0.117	0.740	0.908	0.723

NoSenseAtAll	0.982	0.905	0.951	0.827	0.908	0.908

gg2	0.327	0.803	0.117	0.740	0.908	0.723

RobustForensics	0.991	0.891	0.951	0.807	0.904	0.902

AllDataRes_EB5	0.964	0.818	0.845	0.647	0.900	0.852

b7_e2_ensemble_chun	0.927	0.759	0.806	0.687	0.900	0.842

ENbsoftFW1111	0.982	0.891	0.922	0.787	0.898	0.892

ATDETECTOR	0.955	0.796	0.922	0.780	0.898	0.875

winer_ensemble_correct2	0.873	0.745	0.816	0.560	0.896	0.814

PredictFake	0.973	0.847	0.913	0.820	0.894	0.887

lcy	0.964	0.883	0.893	0.573	0.890	0.850

yangjiezhi-ensemble	0.927	0.745	0.816	0.700	0.884	0.835

Aquarius	1.000	0.854	0.971	0.807	0.884	0.890

StableForensics	0.991	0.847	0.951	0.787	0.884	0.883

effb7_newdataaug_v7_aligne_v2	0.945	0.810	0.942	0.613	0.884	0.846

InTeLe_	1.000	0.796	0.922	0.720	0.884	0.864

b7_514_e2_luo_yang_efficienet	0.936	0.825	0.854	0.700	0.884	0.851

en7_yjz	0.973	0.898	0.922	0.720	0.880	0.873

FullEfficientNet	0.836	0.526	0.728	0.480	0.880	0.751

ZFake	0.945	0.737	0.864	0.693	0.880	0.838

yjzenb_lowqulity	0.973	0.905	0.932	0.727	0.880	0.876

696_723_yjz	0.964	0.818	0.893	0.740	0.878	0.860

EnbFW	0.973	0.912	0.951	0.727	0.878	0.878

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

EfficienNetb7	0.973	0.905	0.922	0.720	0.868	0.868

result_all_best_th0.85_517_ens	0.945	0.847	0.854	0.740	0.866	0.852

HKU_EfficientNet-B7_v2	0.845	0.606	0.806	0.447	0.864	0.758

framelevel_all_compress_format	0.782	0.124	0.155	0.173	0.860	0.575

Yolo Object detection	0.864	0.628	0.757	0.667	0.854	0.786

shangci	0.973	0.876	0.942	0.747	0.854	0.863

KBNet	0.909	0.774	0.854	0.793	0.852	0.839

HRC	0.827	0.613	0.573	0.660	0.848	0.757

iouiwc-31	0.964	0.796	0.893	0.760	0.848	0.845

EfficientB5_OHEM	0.918	0.876	0.913	0.653	0.848	0.837

LGSC_Lite	0.955	0.839	0.874	0.773	0.844	0.848

Yjz_jerry	0.973	0.876	0.893	0.713	0.840	0.846

focbe	0.900	0.832	0.786	0.793	0.828	0.827

IAMFAKE2	0.464	0.496	0.515	0.367	0.826	0.640

IAMFAKE	0.436	0.409	0.524	0.300	0.816	0.611

unet+res	0.882	0.832	0.796	0.767	0.812	0.814

UltraVision	0.918	0.847	0.932	0.733	0.810	0.828

YJZ_419_513_ensemble	0.964	0.832	0.951	0.780	0.808	0.839

fakeface	0.955	0.766	0.883	0.747	0.806	0.816

effb7_newdataaug_v7_aligne	0.973	0.825	0.961	0.660	0.804	0.820

YJZ_ensemmble	0.936	0.810	0.942	0.753	0.794	0.821

EfficientB4_380	0.882	0.701	0.796	0.740	0.794	0.783

IR-Capsule	0.973	0.818	0.942	0.793	0.792	0.831

RealFace	0.945	0.766	0.864	0.813	0.790	0.815

IR+unet	0.909	0.847	0.845	0.787	0.790	0.816

resnet50	0.527	0.504	0.408	0.387	0.786	0.620

Xceptio InceptionRes Efficient	0.964	0.825	0.864	0.827	0.768	0.816

inceptionv1	0.655	0.350	0.544	0.333	0.768	0.610

antifake	0.936	0.803	0.942	0.687	0.764	0.795

SCAN	0.909	0.825	0.738	0.613	0.764	0.763

yd dust face	0.955	0.861	0.951	0.693	0.762	0.806

framelevel	0.873	0.737	0.835	0.760	0.760	0.777

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
SiameseDF	0.864	0.723	0.709	0.667	0.750	0.742

DFSpot-Ensemble	0.900	0.766	0.825	0.700	0.750	0.769

Siam-ensemble	0.945	0.934	0.951	0.747	0.748	0.816

simple policy	0.955	0.869	0.942	0.387	0.744	0.751

face single model	0.964	0.869	0.942	0.460	0.738	0.760

faceClassify1	0.945	0.854	0.981	0.793	0.730	0.806

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
swap_classify	0.909	0.759	0.864	0.747	0.724	0.767

EfficientNet-b4	0.955	0.796	0.825	0.827	0.712	0.779

EffB7_DataAug_v1	0.945	0.847	0.825	0.813	0.712	0.783

EffB7_DataAug_v7	0.964	0.920	0.951	0.867	0.710	0.815

AttentionExcep	0.782	0.723	0.524	0.747	0.700	0.701

LVLNet	0.964	0.752	0.796	0.687	0.694	0.741

xceptattention	0.791	0.774	0.456	0.747	0.680	0.692

EfficientNetTest	0.709	0.445	0.631	0.387	0.674	0.599

EffB8_FFDFDCDFCelebAISG	0.382	0.409	0.447	0.433	0.660	0.539

Xception_ForgeryNet	0.818	0.496	0.544	0.407	0.632	0.591

EfficientNetB7	0.836	0.847	0.874	0.747	0.630	0.725

PPPP	0.827	0.635	0.621	0.480	0.628	0.628

Sentinel	0.964	0.905	0.883	0.867	0.624	0.763

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

MMIG-Net	0.909	0.825	0.816	0.887	0.566	0.713

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
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
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
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
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,
DFirt	0.982	0.942	0.961	0.893	0.494	0.717

YSNet	0.564	0.584	0.592	0.427	0.492	0.513

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
EfficientNet-v0	0.827	0.730	0.806	0.533	0.420	0.564

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
eff-b7-v3	0.973	0.912	0.913	0.807	0.198	0.546

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

SRTNet_formal_v2	0.982	0.956	0.951	0.960	0.066	0.514