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Distinguishing Between Natural and gan-generated Face Images by Combining Global and Local Features

Chinese Journal of Electronics 2022年第1期31卷 59-67页

作者： CHEN Beijing TAN Weijin WANG Yiting ZHAO Guoying Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology School of Computer Nanjing University of Information Science and Technology Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science and Technology Warwick Manufacturing Group University of Warwick Center for Machine Vision and Signal Analysis University of Oulu

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 *** 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： feature extraction neural nets gan-generated datasets local features metric learning generative adversarial networks face image gan-generated faces generation technology learning (artificial intelligence) discriminative features gan-generated face images face recognition face image synthesis generalization performance global features face key-points

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