Contrast Modification Forensics Algorithm Based on Merged Weight Histogram of Run Length
Yan Xuan and
Additional contact information
Liang Yang: Nankai University, Tianjin, China
Tiegang Gao: College of Software, Nankai University, Tianjin, China
Yan Xuan: Nankai University, Tianjin, China
Hang Gao: Nankai University, Tianjin, China
International Journal of Digital Crime and Forensics (IJDCF), 2016, vol. 8, issue 2, 27-35
A novel image forensic algorithm against contrast modification based on merged weight histogram of run length is proposed. In the proposed algorithm, the run length histogram features were firstly extracted, and then those of different orientation were subsequently merged; after normalization of the prior features, the authors calculated leaps in the histogram numerically; lastly, the generated features of authentic and tampered images were trained by a SVM classifier. Large amounts of experiments show that, the proposed algorithm has low cost of computation complexity, compared with some existing scheme, and it has better performance with many test databases, furthermore, the proposed algorithm can effectively detect local contrast modification of image.
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJDCF.2016040103 (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:igg:jdcf00:v:8:y:2016:i:2:p:27-35
Access Statistics for this article
More articles in International Journal of Digital Crime and Forensics (IJDCF) from IGI Global
Bibliographic data for series maintained by Journal Editor ().