H. Zhang, B. Chen, J. Wang and G. Zhao, "A Local Perturbation Generation Method for GAN-Generated Face Anti-Forensics," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 2, pp. 661-676, Feb. 2023, doi: 10.1109/TCSVT.2022.3207310
A local perturbation generation method for GAN-generated face anti-forensics
|Author:||Zhang, Haitao1,2,3; Chen, Beijing1,2,3; Wang, Jinwei1,2,4;|
1Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
2School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China
3Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China
4Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, ChinaJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China
5Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, FI, Finland
|Online Access:||PDF Full Text (PDF, 1.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023030329564
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-03-03
Although the current generative adversarial networks (GAN)-generated face forensic detectors based on deep neural networks (DNNs) have achieved considerable performance, they are vulnerable to adversarial attacks. In this paper, an effective local perturbation generation method is proposed to expose the vulnerability of state-of-the-art forensic detectors. The main idea is to mine the fake faces’ areas of common concern in multiple-detectors’ decision-making, then generate local anti-forensic perturbations by GANs in these areas to enhance the visual quality and transferability of anti-forensic faces. Meanwhile, in order to improve the anti-forensic effect, a double- mask (soft mask and hard mask) strategy and a three-part loss (the GAN training loss, the adversarial loss consisting of ensemble classification loss and ensemble feature loss, and the regularization loss) are designed for the training of the generator. Experiments conducted on fake faces generated by StyleGAN demonstrate the proposed method’s advantage over the state-of-the-art methods in terms of anti-forensic success rate, imperceptibility, and transferability. The source code is available at https://github.com/imagecbj/A-Local-Perturbation-Generation-Method-for-GAN-generated-Face-Anti-forensics.
IEEE transactions on circuits and systems for video technology
|Pages:||661 - 676|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This work was supported in part by the National Natural Science Foundation of China under Grants 62072251, and 62072250; in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund.
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