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

文献类型：期刊论文

作者Zhang, Rui-Song1,2; Quan, Wei-Ze1,2; Fan, Lu-Bin4; Hu, Li-Ming3; Yan, Dong-Ming1,2,3
刊名JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
出版日期2020-05-01
卷号35期号:3页码:592-602
关键词natural image computer-generated image channel and pixel correlation convolutional neural network
ISSN号1000-9000
DOI10.1007/s11390-020-0216-9
通讯作者Yan, Dong-Ming(yandongming@gmail.com)
英文摘要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.
WOS关键词GRAPHICS ; IDENTIFICATION ; CLASSIFICATION
资助项目National Key Research and Development Program of China[2019YFB2204104] ; Beijing Natural Science Foundation of China[L182059] ; National Natural Science Foundation of China[61772523] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[61802406] ; Alibaba Group through Alibaba Innovative Research Program ; 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
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000539025300009
出版者SCIENCE PRESS
资助机构National Key Research and Development Program of China ; Beijing Natural Science Foundation of China ; National Natural Science Foundation of China ; Alibaba Group through Alibaba Innovative Research Program ; 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
源URL[http://ir.ia.ac.cn/handle/173211/39838]  
专题模式识别国家重点实验室_三维可视计算
通讯作者Yan, Dong-Ming
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China
4.Alibaba Grp, Hangzhou 310023, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Rui-Song,Quan, Wei-Ze,Fan, Lu-Bin,et al. Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2020,35(3):592-602.
APA Zhang, Rui-Song,Quan, Wei-Ze,Fan, Lu-Bin,Hu, Li-Ming,&Yan, Dong-Ming.(2020).Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,35(3),592-602.
MLA Zhang, Rui-Song,et al."Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 35.3(2020):592-602.

入库方式： OAI收割

来源：自动化研究所

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