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Image Splicing Localization via Semi-Global Network and Fully Connected Conditional Random Fields
Xiaodong Cun; Chi-Man Pun
2018
Conference NameProceedings of European Conference on Computer Vision (ECCV) Workshops, 2018.
Source PublicationProceedings of European Conference on Computer Vision (ECCV) Workshops, 2018.
Conference Date2018-9-8
Conference PlaceMunich, Germany
Abstract

We address the problem of image splicing localization: given an input image, localizing the spliced region which is cut from another image. We formulate this as a classification task but, critically, instead of classifying the spliced region by local patch, we leverage the features from whole image and local patch together to classify patch. We call this structure Semi-Global Network. Our approach exploits the observation that the spliced region should not only highly relate to local features (spliced edges), but also global features (semantic information, illumination, etc.) from the whole image. Furthermore, we first integrate Fully Connected Conditional Random Fields as post-processing technique in image splicing to improve the consistency between the input image and the output of the network. We show that our method outperforms other state-of-the-art methods in three popular datasets.

KeywordImage Splicing Localization Image Forgery Localization Multimedia Security
URLView the original
Indexed BySCI
Language英语
Fulltext Access
Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationUniversity of Macau, Taipa, Macau
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Xiaodong Cun,Chi-Man Pun. Image Splicing Localization via Semi-Global Network and Fully Connected Conditional Random Fields[C],2018.
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