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Multi-Level Dense Descriptor and Hierarchical Feature Matching for Copy-Move Forgery Detection
Bi X.; Pun C.-M.; Yuan X.-C.
2016-02-04
Source PublicationInformation Sciences
ISSN00200255
Volume345Pages:226-242
Abstract

In this paper, a Multi-Level Dense Descriptor (MLDD) extraction method and a Hierarchical Feature Matching method are proposed to detect copy-move forgery in digital images. The MLDD extraction method extracts the dense feature descriptors using multiple levels, while the extracted dense descriptor consists of two parts: the Color Texture Descriptor and the Invariant Moment Descriptor. After calculating the MLDD for each pixel, the Hierarchical Feature Matching method subsequently detects forgery regions in the input image. First, the pixels that have similar color textures are grouped together into distinctive neighbor pixel sets. Next, each pixel is matched with pixels in its corresponding neighbor pixel set through its geometric invariant moments. Then, the redundant pixels from previously generated matched pixel pairs are filtered out by the proposed Adaptive Distance and Orientation Based Filtering method. Finally, some morphological operations are applied to generate the final detected forgery regions. Experimental results show that the proposed scheme can achieve much better detection results compared with the existing state-of-the-art CMFD methods, even under various challenging conditions such as geometric transforms, JPEG compression, noise addition and down-sampling.

KeywordColor Texture Descriptor Copy-move Forgery Detection (Cmfd) Hierarchical Feature Matching Invariant Moment Descriptor Multi-level Dense Descriptor (Mldd)
DOI10.1016/j.ins.2016.01.061
URLView the original
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000372687300016
PublisherELSEVIER SCIENCE INC
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Citation statistics
Cited Times [WOS]:36   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorPun C.-M.
AffiliationDepartment of Computer and Information Science, University of Macau, Macau SAR, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Bi X.,Pun C.-M.,Yuan X.-C.. Multi-Level Dense Descriptor and Hierarchical Feature Matching for Copy-Move Forgery Detection[J]. Information Sciences,2016,345:226-242.
APA Bi X.,Pun C.-M.,&Yuan X.-C..(2016).Multi-Level Dense Descriptor and Hierarchical Feature Matching for Copy-Move Forgery Detection.Information Sciences,345,226-242.
MLA Bi X.,et al."Multi-Level Dense Descriptor and Hierarchical Feature Matching for Copy-Move Forgery Detection".Information Sciences 345(2016):226-242.
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