Distinguishing Between Natural and GAN-Generated Face Images by Combining Global and Local Features

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外文摘要：With the development of face image syn-thesis and generation technology based on generative ad-versarial networks(GANs),it has become a research hot-spot to determine whether a given face image is natural or generated.However,the generalization capability of the existing algorithms is still to be improved.Therefore,this paper proposes a general algorithm.To do so,firstly,the learning on important local areas,containing many face key-points,is strengthened by combining the global and local features.Secondly,metric learning based on the ArcFace loss is applied to extract common and discrimin-ative features.Finally,the extracted features are fed into the classification module to detect GAN-generated faces.The experiments are conducted on two publicly available natural datasets(CelebA and FFHQ)and seven GAN-generated datasets.Experimental results demonstrate that the proposed algorithm achieves a better generalization performance with an average detection accuracy over 0.99 than the state-of-the-art algorithms.Moreover,the pro-posed algorithm is robust against additional attacks,such as Gaussian blur,and Gaussian noise addition.

外文关键词：

Generated imageGlobal featureLoc-al featuresGenerative adversarial networkMetric learning

作者：

CHEN Beijing、TAN Weijin、WANG Yiting、ZHAO Guoying

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作者单位：

Engineering Research Center of Digital Forensics,Ministry of Education,Nanjing University of Information Science and Technology,Nanjing 210044,China

School of Computer,Nanjing University of Information Science and Technology,Nanjing 210044,China

Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET),Nanjing University of Information Science and Technology,Nanjing 210044,China

Warwick Manufacturing Group,University of Warwick,Coventry CV4 7AL,UK

Center for Machine Vision and Signal Analysis,University of Oulu,Oulu 90014,Finland

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基金：

国家自然科学基金NUIST Students'Platform for Innovation and Entrepreneurship Training ProgramPriority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund

项目编号：

62072251202110300022Z

出版年：

2022

DOI：

10.1049/cje.2020.00.372

电子学报(英文)

CSTPCDSCIEI

ISSN：1022-4653

年,卷(期)：2022.31(1)

被引量1
参考文献量3