Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique

In this thesis, the effect of feature type and matching method has been analyzed by comparing different combinations of matching method – feature type for copy-move image forgery detection. The results showed an interaction between some of the features and some of the matching methods. Due to the im...

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Main Author:	Mohammed Abdo Al-Qershi, Osamah
Format:	Thesis
Language:	English
Published:	2018
Subjects:	T Technology
Online Access:	http://eprints.usm.my/56080/1/Enhanced%20Block-Based%20Copy-Move%20Image%20Forgery%20Detection%20Using%20K-Means%20Clustering%20Technique_Osamah%20Mohammed%20Abdo%20Al-Qershi.pdf
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spelling	my-usm-ep.560802022-12-21T07:35:09Z Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique 2018-06-01 Mohammed Abdo Al-Qershi, Osamah T Technology TK Electrical Engineering. Electronics. Nuclear Engineering In this thesis, the effect of feature type and matching method has been analyzed by comparing different combinations of matching method – feature type for copy-move image forgery detection. The results showed an interaction between some of the features and some of the matching methods. Due to the importance of matching process, this thesis focused on improving the matching process by proposing an enhanced block-based copy-move forgery detection pipeline. The proposed pipeline relied on clustering the image blocks into clusters, and then independently performing the matching of the blocks within each cluster which will reduce the time required for matching and increase the true positive ratio (TPR) as well. In order to deploy the proposed pipeline, two combinations of matching method - feature type are considered. In the first case, Zernike Moments (ZMs) were combined with Locality Sensitive Hashing (LSH) and tested on three datasets. The experimental results showed that the proposed pipeline reduced the processing time by 73.05% to 84.70% and enhanced the accuracy of detection by 5.56% to 25.43%. In the second case, Polar Cosine Transform (PCT) was combined with Lexicographical Sort (LS). Although the proposed pipeline could not reduce the processing time, it enhanced the accuracy of detection by 32.46%. The obtained results were statistically analyzed, and it was proven that the proposed pipeline can enhance the accuracy of detection significantly based on the comparison with other two methods. 2018-06 Thesis http://eprints.usm.my/56080/ http://eprints.usm.my/56080/1/Enhanced%20Block-Based%20Copy-Move%20Image%20Forgery%20Detection%20Using%20K-Means%20Clustering%20Technique_Osamah%20Mohammed%20Abdo%20Al-Qershi.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	T Technology T Technology
spellingShingle	T Technology T Technology Mohammed Abdo Al-Qershi, Osamah Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique
description	In this thesis, the effect of feature type and matching method has been analyzed by comparing different combinations of matching method – feature type for copy-move image forgery detection. The results showed an interaction between some of the features and some of the matching methods. Due to the importance of matching process, this thesis focused on improving the matching process by proposing an enhanced block-based copy-move forgery detection pipeline. The proposed pipeline relied on clustering the image blocks into clusters, and then independently performing the matching of the blocks within each cluster which will reduce the time required for matching and increase the true positive ratio (TPR) as well. In order to deploy the proposed pipeline, two combinations of matching method - feature type are considered. In the first case, Zernike Moments (ZMs) were combined with Locality Sensitive Hashing (LSH) and tested on three datasets. The experimental results showed that the proposed pipeline reduced the processing time by 73.05% to 84.70% and enhanced the accuracy of detection by 5.56% to 25.43%. In the second case, Polar Cosine Transform (PCT) was combined with Lexicographical Sort (LS). Although the proposed pipeline could not reduce the processing time, it enhanced the accuracy of detection by 32.46%. The obtained results were statistically analyzed, and it was proven that the proposed pipeline can enhance the accuracy of detection significantly based on the comparison with other two methods.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohammed Abdo Al-Qershi, Osamah
author_facet	Mohammed Abdo Al-Qershi, Osamah
author_sort	Mohammed Abdo Al-Qershi, Osamah
title	Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique
title_short	Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique
title_full	Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique
title_fullStr	Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique
title_full_unstemmed	Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique
title_sort	enhanced block-based copy-move image forgery detection using k-means clustering technique
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik dan Elektronik
publishDate	2018
url	http://eprints.usm.my/56080/1/Enhanced%20Block-Based%20Copy-Move%20Image%20Forgery%20Detection%20Using%20K-Means%20Clustering%20Technique_Osamah%20Mohammed%20Abdo%20Al-Qershi.pdf
_version_	1776101129846784000

Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique

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