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

ENbsoftFW	0.982	0.891	0.922	0.787	0.898	0.892

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

EnbFW	0.973	0.912	0.951	0.727	0.878	0.878

FAKEDET	0.964	0.832	0.922	0.687	0.918	0.877

yjzenb_lowqulity	0.973	0.905	0.932	0.727	0.880	0.876

ATDETECTOR	0.955	0.796	0.922	0.780	0.898	0.875

en7_yjz	0.973	0.898	0.922	0.720	0.880	0.873

EfficienNetb7	0.973	0.905	0.922	0.720	0.868	0.868

InTeLe_	1.000	0.796	0.922	0.720	0.884	0.864

shangci	0.973	0.876	0.942	0.747	0.854	0.863

696_723_yjz	0.964	0.818	0.893	0.740	0.878	0.860

CONG	0.955	0.810	0.854	0.780	0.874	0.858

Balance	0.918	0.861	0.883	0.733	0.876	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

result_all_best_th0.85_517_ens	0.945	0.847	0.854	0.740	0.866	0.852

b7_514_e2_luo_yang_efficienet	0.936	0.825	0.854	0.700	0.884	0.851

lcy	0.964	0.883	0.893	0.573	0.890	0.850

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

effb7_newdataaug_v7_aligne_v2	0.945	0.810	0.942	0.613	0.884	0.846

iouiwc-31	0.964	0.796	0.893	0.760	0.848	0.845

b7_e2_ensemble_chun	0.927	0.759	0.806	0.687	0.900	0.842

KBNet	0.909	0.774	0.854	0.793	0.852	0.839

YJZ_419_513_ensemble	0.964	0.832	0.951	0.780	0.808	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

Yjz	0.964	0.664	0.942	0.553	0.920	0.837

yangjiezhi-ensemble	0.927	0.745	0.816	0.700	0.884	0.835

IR-Capsule	0.973	0.818	0.942	0.793	0.792	0.831

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

YJZ_ensemmble	0.936	0.810	0.942	0.753	0.794	0.821

effb7_newdataaug_v7_aligne	0.973	0.825	0.961	0.660	0.804	0.820

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

Xceptio InceptionRes Efficient	0.964	0.825	0.864	0.827	0.768	0.816

RealFace	0.945	0.766	0.864	0.813	0.790	0.815

EffB7_DataAug_v7	0.964	0.920	0.951	0.867	0.710	0.815

unet+res	0.882	0.832	0.796	0.767	0.812	0.814

winer_ensemble_correct2	0.873	0.745	0.816	0.560	0.896	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
yd dust face	0.955	0.861	0.951	0.693	0.762	0.806

faceClassify1	0.945	0.854	0.981	0.793	0.730	0.806

Yolo with Voting Mechanism	0.855	0.606	0.709	0.587	0.916	0.796

antifake	0.936	0.803	0.942	0.687	0.764	0.795

Yolo Object detection	0.864	0.628	0.757	0.667	0.854	0.786

EfficientB4_380	0.882	0.701	0.796	0.740	0.794	0.783

EffB7_DataAug_v1	0.945	0.847	0.825	0.813	0.712	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

SCAN	0.909	0.825	0.738	0.613	0.764	0.763

Sentinel	0.964	0.905	0.883	0.867	0.624	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

FullEfficientNet	0.836	0.526	0.728	0.480	0.880	0.751

SiameseDF	0.864	0.723	0.709	0.667	0.750	0.742

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

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

DFirt	0.982	0.942	0.961	0.893	0.494	0.717

MMIG-Net	0.909	0.825	0.816	0.887	0.566	0.713

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

lcy2	0.900	0.073	0.845	0.053	0.950	0.679

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

YjzV4	0.200	0.153	0.107	0.707	0.938	0.629

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
Top8-soft-withoutnt	0.300	0.066	0.913	0.007	0.974	0.624

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

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

EfficientNet-v0	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