Image Splicing Detection via Camera Response Function Analysis

doi:10.1109/CVPR.2017.203

	Image Splicing Detection via Camera Response Function Analysis
	Chen, Can 1; McCloskey, Scott 2; Yu, Jingyi1,3
	2017
会议录名称	30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
卷号	2017-January
页码	1876-1885
发表状态	已发表
DOI	10.1109/CVPR.2017.203
摘要	Recent advances in image manipulation tools have made image forgery detection increasingly more challenging. An important component in such tools is the ability to fake blur to hide splicing and copy-move operations. In this paper, we present a new technique based on the analysis of the camera response functions (CRF) for efficient and robust splicing and copy-move forgery detection and localization. We first analyze how non-linear CRFs affect edges in terms of the intensity-gradient bivariate histograms. We show distinguishable shape differences between real and forged blurs near edges after a splicing operation. Based on our analysis, we introduce a deep-learning framework to detect and localize forged edges. In particular, we show the problem can be transformed to a handwriting recognition problem and resolved by using a convolutional neural network. We generate a large dataset of forged images produced by splicing followed by retouching and comprehensive experiments show our proposed method outperforms the state-of-the-art techniques in accuracy and robustness.
出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
会议地点	Honolulu, HI, United states
收录类别	CPCI ; EI
语种	英语
资助项目	Defense Advanced Research Projects Agency[FA8750-16-C-0190]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000418371401098
出版者	IEEE
EI入藏号	20181304947673
EI主题词	Cameras ; Character recognition ; Computer vision ; Deep learning ; Neural networks
EI分类号	Computer Applications:723.5 ; Photographic Equipment:742.2
WOS关键词	EXPOSING DIGITAL FORGERIES ; LOCALIZATION ; FORENSICS
原始文献类型	Proceedings Paper
引用统计
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/16320
专题	信息科学与技术学院_PI研究组_虞晶怡组
通讯作者	Chen, Can
作者单位	1.Univ Delaware, Newark, DE 19716 USA 2.Honeywell ACS Labs, Golden Valley, MN USA 3.ShanghaiTech Univ, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Chen, Can,McCloskey, Scott,Yu, Jingyi. Image Splicing Detection via Camera Response Function Analysis[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:1876-1885.