Image Splicing Detection via Camera Response Function Analysis
Chen, Can1; McCloskey, Scott2; Yu, Jingyi1,3
2017
Source Publication30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
Volume2017-January
Pages1876-1885
DOI10.1109/CVPR.2017.203
AbstractRecent 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.
Conference PlaceHonolulu, HI, United states
Indexed ByCPCI ; EI
Language英语
Funding ProjectDefense Advanced Research Projects Agency[FA8750-16-C-0190]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000418371401098
PublisherIEEE
EI Accession Number20181304947673
EI KeywordsCameras
EI Classification NumberComputer Applications:723.5 ; Photographic Equipment:742.2
WOS KeywordEXPOSING DIGITAL FORGERIES ; LOCALIZATION ; FORENSICS
Original Document TypeProceedings Paper
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://kms.shanghaitech.edu.cn/handle/2MSLDSTB/16320
Collection信息科学与技术学院_PI研究组_虞晶怡组
Corresponding AuthorChen, Can
Affiliation1.Univ Delaware, Newark, DE 19716 USA
2.Honeywell ACS Labs, Golden Valley, MN USA
3.ShanghaiTech Univ, Shanghai, Peoples R China
Recommended Citation
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.
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