Open AccessAn anti-forensic scheme on computer graphic images and natural images using generative adversarial networks
Author(s) -
Qi Cui,
Ruo Han Meng,
Zhuangzhuang Zhou,
Xing Sun,
Kai Zhu
Publication year - 2019
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2019248
Subject(s) - computer science , generative adversarial network , adversarial system , generative grammar , scheme (mathematics) , identification (biology) , artificial intelligence , image (mathematics) , computer vision , pattern recognition (psychology) , mathematical analysis , botany , mathematics , biology
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.