Publication:
Forgery detection algorithm based on texture features

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dc.citedby3
dc.contributor.authorAhmed I.T.en_US
dc.contributor.authorHammad B.T.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorid57193324906en_US
dc.contributor.authorid57193327622en_US
dc.contributor.authorid36682671900en_US
dc.date.accessioned2023-05-29T09:05:49Z
dc.date.available2023-05-29T09:05:49Z
dc.date.issued2021
dc.description.abstractAny researcher's goal is to improve detection accuracy with a limited feature vector dimension. Therefore, in this paper, we attempt to find and discover the best types of texture features and classifiers that are appropriate for the coarse mesh finite differenc (CMFD). Segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and Haralick are the texture features that have been chosen. K-nearest neighbors (KNN), na�ve Bayes, and Logistics are also among the classifiers chosen. SFTA, LBP, and Haralick feature vector are fed to the KNN, na�ve Bayes, and logistics classifier. The outcomes of the experiment indicate that the SFTA texture feature surpassed all other texture features in all classifiers, making it the best texture feature to use in forgery detection. Haralick feature has the second-best texture feature performance in all of the classifiers. The performance using the LBP feature is lower than that of the other texture features. It also shows that the KNN classifier outperformed the other two in terms of accuracy. However, among the classifiers, the logistic classifier had the lowest accuracy. The proposed SFTA based KNN method is compared to other state-of-the-art techniques in terms of feature dimension and detection accuracy. The proposed method outperforms other current techniques. � 2021 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v24.i1.pp226-235
dc.identifier.epage235
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85116528024
dc.identifier.spage226
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85116528024&doi=10.11591%2fijeecs.v24.i1.pp226-235&partnerID=40&md5=20c52d967245f33e970ab66a01532061
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25968
dc.identifier.volume24
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleForgery detection algorithm based on texture featuresen_US
dc.typeArticleen_US
dspace.entity.typePublication
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