Improving RLRN Image Splicing Detection with the Use of PCA and Kernel PCA
Joint Authors
Jalab, Hamid A.
Moghaddasi, Zahra
Md Noor, Rafidah
Aghabozorgi, Said
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-11
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Digital image forgery is becoming easier to perform because of the rapid development of various manipulation tools.
Image splicing is one of the most prevalent techniques.
Digital images had lost their trustability, and researches have exerted considerable effort to regain such trustability by focusing mostly on algorithms.
However, most of the proposed algorithms are incapable of handling high dimensionality and redundancy in the extracted features.
Moreover, existing algorithms are limited by high computational time.
This study focuses on improving one of the image splicing detection algorithms, that is, the run length run number algorithm (RLRN), by applying two dimension reduction methods, namely, principal component analysis (PCA) and kernel PCA.
Support vector machine is used to distinguish between authentic and spliced images.
Results show that kernel PCA is a nonlinear dimension reduction method that has the best effect on R, G, B, and Y channels and gray-scale images.
American Psychological Association (APA)
Moghaddasi, Zahra& Jalab, Hamid A.& Md Noor, Rafidah& Aghabozorgi, Said. 2014. Improving RLRN Image Splicing Detection with the Use of PCA and Kernel PCA. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1050310
Modern Language Association (MLA)
Moghaddasi, Zahra…[et al.]. Improving RLRN Image Splicing Detection with the Use of PCA and Kernel PCA. The Scientific World Journal No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1050310
American Medical Association (AMA)
Moghaddasi, Zahra& Jalab, Hamid A.& Md Noor, Rafidah& Aghabozorgi, Said. Improving RLRN Image Splicing Detection with the Use of PCA and Kernel PCA. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1050310
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050310