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

作　　者：CHEN Beijing TAN Weijin WANG Yiting ZHAO Guoying 

机构地区：[1]Engineering Research Center of Digital Forensics,Ministry of Education,Nanjing University of Information Science and Technology,Nanjing 210044,China [2]School of Computer,Nanjing University of Information Science and Technology,Nanjing 210044,China [3]Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET),Nanjing University of Information Science and Technology,Nanjing 210044,China [4]Warwick Manufacturing Group,University of Warwick,Coventry CV47AL,UK [5]Center for Machine Vision and Signal Analysis,University of Oulu,Oulu 90014,Finland

出　　处：《Chinese Journal of Electronics》2022年第1期59-67,共9页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(62072251);NUIST Students’Platform for Innovation and Entrepreneurship Training Program(202110300022Z);the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund。

摘　　要：With the development of face image synthesis and generation technology based on generative adversarial networks(GANs), it has become a research hotspot 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 Arc Face loss is applied to extract common and discriminative 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(Celeb A and FFHQ) and seven GANgenerated 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 proposed algorithm is robust against additional attacks, such as Gaussian blur, and Gaussian noise addition.

关 键 词：Generated image Global feature Local features Generative adversarial network Metric learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]