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• Artificial Intelligence and Pattern Recognition •

Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation

Rui-Song Zhang1,2, Wei-Ze Quan1,2, Lu-Bin Fan3, Li-Ming Hu4, Dong-Ming Yan1,2,4,*, Senior Member, CCF, Member, ACM, IEEE

1. 1 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
2 School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China;
3 Alibaba Group, Hangzhou 310023, China;
4 State Key Laboratory of Hydro-Science and Engineering, Tsinghua University, Beijing 100084, China
• Received:2019-12-10 Revised:2020-01-05 Online:2020-05-28 Published:2020-05-28
• Contact: Dong-Ming Yan E-mail:yandongming@gmail.com
• About author:Rui-Song Zhang is currently pursuing his M.S. degree in the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing. He received his B.S. degree in information security from Xidian University, Xi'an, in 2019. His research interests include image processing and image forensics.
• Supported by:
This work was supported by the National Key Research and Development Program of China under Grant No. 2019YFB2204104, the Beijing Natural Science Foundation of China under Grant No. L182059, the National Natural Science Foundation of China under Grant Nos. 61772523, 61620106003, and 61802406, Alibaba Group through Alibaba Innovative Research Program, and the Joint Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering and Tsinghua-Ningxia Yinchuan Joint Institute of Internet of Waters on Digital Water Governance.

With the recent tremendous advances of computer graphics rendering and image editing technologies, computergenerated fake images, which in general do not reflect what happens in the reality, can now easily deceive the inspection of human visual system. In this work, we propose a convolutional neural network (CNN)-based model to distinguish computergenerated (CG) images from natural images (NIs) with channel and pixel correlation. The key component of the proposed CNN architecture is a self-coding module that takes the color images as input to extract the correlation between color channels explicitly. Unlike previous approaches that directly apply CNN to solve this problem, we consider the generality of the network (or subnetwork), i.e., the newly introduced hybrid correlation module can be directly combined with existing CNN models for enhancing the discrimination capacity of original networks. Experimental results demonstrate that the proposed network outperforms state-of-the-art methods in terms of classification performance. We also show that the newly introduced hybrid correlation module can improve the classification accuracy of different CNN architectures.

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