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Learn with diversity and from harder samples: Improving the generalization of CNN-Based detection of computer-generated images

文献类型：期刊论文

作者Quan, Weize1,2,3; Wang, Kai3; Yan, Dong-Ming1,2; Zhang, Xiaopeng1,2; Pellerin, Denis3
刊名FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION
出版日期2020-12-01
卷号35页码:12
关键词Image forensics Computer-generated image Convolutional neural network Generalization Negative samples
DOI10.1016/j.fsidi.2020.301023
通讯作者Yan, Dong-Ming(yandongming@gmail.com)
英文摘要Advanced computer graphics rendering software tools can now produce computer-generated (CG) images with increasingly high level of photorealism. This makes it more and more difficult to distinguish natural images (Nis) from CG images by naked human eyes. For this forensic problem, recently some CNN(convolutional neural network)-based methods have been proposed. However, researchers rarely pay attention to the blind detection (or generalization) problem, i.e., no training sample is available from "unknown" computer graphics rendering tools that we may encounter during the testing phase. We observe that detector performance decreases, sometimes drastically, in this challenging but realistic setting. To study this challenging problem, we first collect four high-quality CG image datasets, which will be appropriately released to facilitate the relevant research. Then, we design a novel two-branch network with different initializations in the first layer to capture diverse features. Moreover, we introduce a gradient-based method to construct harder negative samples and conduct enhanced training to further improve the generalization of CNN-based detectors. Experimental results demonstrate the effectiveness of our method in improving the performance for the challenging task of "blind" detection of CG images. (C) 2020 Elsevier Ltd. All rights reserved.
WOS关键词NATURAL IMAGES ; DOMAIN
资助项目National Key R&D Program of China[2019YFB2204104] ; National Key R&D Program of China[2018YFB2100602] ; National Natural Science Foundation of China[61772523] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[61972459] ; Beijing Natural Science Foundation[L182059] ; French National Agency for Research through PERSYVAL-lab[ANR-11-LABX-002501] ; Joint Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering ; UCAS Joint PhD Training Program ; French National Agency for Research through DEFALS[ANR-16-DEFA-0003] ; 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:000600551900008
出版者ELSEVIER SCI LTD
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; French National Agency for Research through PERSYVAL-lab ; Joint Open Research Fund Program of State Key Laboratory of Hydroscience and Engineering ; UCAS Joint PhD Training Program ; French National Agency for Research through DEFALS ; Tsinghua-Ningxia Yinchuan Joint Institute of Internet of Waters on Digital Water Governance
源URL[http://ir.ia.ac.cn/handle/173211/42795]  
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 NLPR, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Univ Grenoble Alpes, GIPSA Lab, Grenoble INP, CNRS, F-38000 Grenoble, France
推荐引用方式
GB/T 7714		 Quan, Weize,Wang, Kai,Yan, Dong-Ming,et al. Learn with diversity and from harder samples: Improving the generalization of CNN-Based detection of computer-generated images[J]. FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION,2020,35:12.
APA Quan, Weize,Wang, Kai,Yan, Dong-Ming,Zhang, Xiaopeng,&Pellerin, Denis.(2020).Learn with diversity and from harder samples: Improving the generalization of CNN-Based detection of computer-generated images.FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION,35,12.
MLA Quan, Weize,et al."Learn with diversity and from harder samples: Improving the generalization of CNN-Based detection of computer-generated images".FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION 35(2020):12.

入库方式： OAI收割

来源：自动化研究所

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