Knowledge Commons of Institute of Automation,CAS
Distinguishing Computer-Generated Images from Natural Images Using Channel and Pixel Correlation | |
Zhang, Rui-Song1,2; Quan, Wei-Ze1,2; Fan, Lu-Bin3; Hu, Li-Ming4; 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 | |
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. |
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