title: Improving RLRN image splicing detection with the Use of PCA and kernel PCA.
abstract: 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.
authors.affiliation: Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Zahra
authors.identifier: 0000-0001-5824-5076
authors.initials: Z
authors.lastname: Moghaddasi
authors.affiliation: Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Hamid A
authors.initials: HA
authors.lastname: Jalab
authors.affiliation: Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Rafidah
authors.initials: R
authors.lastname: Md Noor
authors.affiliation: Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Saeed
authors.identifier: 0000-0003-1908-4227
authors.initials: S
authors.lastname: Aghabozorgi
mesh_terms: D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000639:trends; D010781:Photography / Q000379:methods* / Q000639:trends; D025341:Principal Component Analysis / Q000379:methods*; D012815:Signal Processing, Computer-Assisted