Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6): 216-224.doi: 10.11896/jsjkx.220400268

• Computer Graphics & Multimedia • Previous Articles     Next Articles

GAN-generated Face Detection Based on Space-Frequency Convolutional Neural Network

WANG Jinwei1,2,3, ZENG Kehui1, ZHANG Jiawei1, LUO Xiangyang3, MA Bin4   

  1. 1 College of Computer,College of Software,College of Cyberspace Security,Nanjing University of Information Science,Technology,Nanjing 210044,China
    2 Jiangsu Collaborative Innovation Center of Atmospheric Environment,Equipment Technology,Nanjing University of Information Science,Technology,Nanjing 210044,China
    3 State Key Laboratory of Mathematical Engineering and Advanced Computing,Zhenzhou 450001,China
    4 Shandong Provincial Key Laboratory of Computer Networks,Qilu University of Technology,Jinan 250353,China
  • Received:2022-04-26 Revised:2022-10-09 Online:2023-06-15 Published:2023-06-06
  • About author:WANG Jinwei,born in 1978,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include artificial intelligence security,color image forensics,color image reversible watermark,robust watermark and image encryption.LUO Xiangyang,born in 1978,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include network and information security.
  • Supported by:
    National Natural Science Foundation of China(62072250,62172435,U1804263,U20B2065,61872203,71802110,61802212),Zhongyuan Science and Technology Innovation Leading Talent Project of China(214200510019),Natural Science Foundation of Jiangsu Province,China(BK20200750),Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(HNTS2022002),Post Graduate Research & Practice Innvoation Program of Jiangsu Province(KYCX200974),Opening Project of Guangdong Province Key Laboratory of Information Security Technology(2020B1212060078),Ministry of Education of Humanities and Social Science Project(19YJA630061) and Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).

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Abstract: The rapid development of generative adversarial networks(GANs) has led to unprecedented success in the field of image generation.The emergence of new GANs such as StyleGAN makes the generated images more realistic and deceptive,posing a greater threat to national security,social stability,and personal privacy.In this paper,a detection algorithm based on a space-frequency joint two-stream convolutional neural network is proposed.Since GAN images will leave clearly discernible artifacts on the spectrum due to the up-sampling operation during the generation process,a learnable frequency-domain filter kernel and frequency domain network are designed to fully learn and extract frequency-domain features.In order to reduce the influence of the information discarded from the image transformation to the frequency domain,a spatial domain network is also designed to learn that the image content itself has differentiated spatial domain features.Finally,the two features are fused to detect the face image generated by GAN.Experimental results on multiple datasets show that the proposed model outperforms existing algorithms in detection accuracy on high-quality generated datasets and generalization across datasets.And for JPEG compression,random cropping,Gaussian blur,and other operations,this method has stronger robustness.In addition,the proposed method also performs well on the local face dataset generated by GAN,which further proves that this model has better generality and wider application prospects.

Key words: Digital image forensics, Face forgery detection, Convolutional neural network, Generative adversarial networks, Frequency domain

CLC Number: 

  • TP391.41
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