An anti-forensic scheme on computer graphic images and natural images using generative adversarial networks

Qi Cui; Ruohan Meng; Zhili Zhou; Xingming Sun; Kaiwen Zhu; Qi Cui; Ruohan Meng; Zhili Zhou; Xingming Sun; Kaiwen Zhu

doi:10.3934/mbe.2019248

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 4923-4935. doi: 10.3934/mbe.2019248

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Research article Special Issues

An anti-forensic scheme on computer graphic images and natural images using generative adversarial networks

1.
School of Computer and Software, Nanjing University of Information Science and Technology, Ning Liu Road, No. 219, Nanjing, 210044, China
2.
Jiangsu Engineering Centre of Network Monitoring, Ning Liu Road, No. 219, Nanjing, 210044, China
3.
School of Engineering and Applied Science, The George Washington University, The Cloyd Heck Marvin Center, 800 21st Street, NW-Suite 505, Washington, DC 20052, US

Received: 19 January 2019 Accepted: 15 May 2019 Published: 31 May 2019

Computer graphic images (CGI) can be manufactured very similar to natural images (NI) by state-of-the-art algorithms in computer graphic filed. Thus, there are various identification algorithms proposed to detect CGI. However, the manipulation is complicated and difficult for an ultimate CGI against the forensic algorithms. Further, the forensics on CGI and NI made achievements in the different aspects with the encouragement of deep learning. Though the generated CGI can achieve high quality automatically by generative adversarial networks (GAN), CGI generation based on GAN is difficult to ensure that it cannot be detected by forensics. In this paper, we propose a brief and effective architecture based on GAN for preventing the generated images being detected under the forensics on CGI and NI. The adapted characteristics will make the CGI generated by GAN fools the detector and keep the end-to-end generation mode of GAN.

Keywords:

Anti-forensics,
GAN,
DCNN,
NI,
CGI

Citation: Qi Cui, Ruohan Meng, Zhili Zhou, Xingming Sun, Kaiwen Zhu. An anti-forensic scheme on computer graphic images and natural images using generative adversarial networks[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 4923-4935. doi: 10.3934/mbe.2019248

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Abstract

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