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

doi:10.1007/s11390-020-0216-9

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 三维可视计算

	Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation
	Zhang, Rui-Song1,2 ; Quan, Wei-Ze1,2 ; Fan, Lu-Bin 3; Hu, Li-Ming 4; Yan, Dong-Ming1,2,4
发表期刊	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
ISSN	1000-9000
	2020-05-01
卷号	35 期号:3 页码:592-602
通讯作者	Yan, Dong-Ming(yandongming@gmail.com)
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.
关键词	natural image computer-generated image channel and pixel correlation convolutional neural network
DOI	10.1007/s11390-020-0216-9
关键词[WOS]	GRAPHICS ; IDENTIFICATION ; CLASSIFICATION
收录类别	SCI
语种	英语
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
项目资助者	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
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS记录号	WOS:000539025300009
出版者	SCIENCE PRESS
七大方向——子方向分类	模式识别基础
引用统计	被引频次：21[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/39839
专题	多模态人工智能系统全国重点实验室_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.Alibaba Grp, Hangzhou 310023, Peoples R China 4.Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
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.