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Dense moment feature index and best match algorithms for video copy-move forgery detection
Zhong,Jun Liu1,2; Pun,Chi Man2; Gan,Yan Fen2,3
2020-10-01
Source PublicationInformation Sciences
ISSN0020-0255
Volume537Pages:184-202
Abstract

This paper proposes a video copy-move forgery detection method to effectively address inter/intra-frame forgeries both at the frame and pixel level. First, a unified moment framework is proposed to extract multi-dimensional dense moment features from the video effectively. Second, a novel feature representation method takes each feature sub-map index to represent its every dimensional feature and then concatenates to a 9-digit dense moment feature index. Third, an inter-frame best match algorithm is proposed to search the 9-digit dense moment feature index of each pixel to find its best matches. All the best matches construct the best match map. Fourth, an inter-frame post-processing algorithm identifies the inter-frame forgery video in the best match map firstly and then indicates the corresponding inter-frame forgery regions. Otherwise, the intra-frame post-processing algorithm re-searches the best match of every pixel in each independent frame and then indicates the intra-frame forgery regions. If the video does not belong to the intra-frame forgeries, the video is determined as a genuine one. The experimental results show that the proposed method is effective at addressing the forensics of the genuine/forgery video and locating the inter/intra-frame copy-move forgeries both at the frame and pixel level.

Keyworda 9-digit Dense Moment Feature Index a Unified Moment Framework Best Match Algorithm Video Copy-move Forgery Detection
DOI10.1016/j.ins.2020.05.134
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000556370700011
Scopus ID2-s2.0-85086385692
Fulltext Access
Citation statistics
Cited Times [WOS]:11   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorPun,Chi Man
Affiliation1.GuangDong Mechanical & Electrical College,Guangzhou,China
2.Department of Computer and Information Science,University of Macau,Macau SAR,China
3.South China Business College Guangdong University of Foreign Studies,China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhong,Jun Liu,Pun,Chi Man,Gan,Yan Fen. Dense moment feature index and best match algorithms for video copy-move forgery detection[J]. Information Sciences,2020,537:184-202.
APA Zhong,Jun Liu,Pun,Chi Man,&Gan,Yan Fen.(2020).Dense moment feature index and best match algorithms for video copy-move forgery detection.Information Sciences,537,184-202.
MLA Zhong,Jun Liu,et al."Dense moment feature index and best match algorithms for video copy-move forgery detection".Information Sciences 537(2020):184-202.
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