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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| Format: | Thesis |
| Language: | English |
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2018
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| Online Access: | 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 |
| _version_ | 1848883257788071936 |
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| author | Mohammed Abdo Al-Qershi, Osamah |
| author_facet | Mohammed Abdo Al-Qershi, Osamah |
| author_sort | Mohammed Abdo Al-Qershi, Osamah |
| building | USM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T18:47:56Z |
| format | Thesis |
| id | usm-56080 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T18:47:56Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-560802022-12-21T07:35:09Z http://eprints.usm.my/56080/ Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique 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. 2018-06-01 Thesis NonPeerReviewed application/pdf en 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 Mohammed Abdo Al-Qershi, Osamah (2018) Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | T Technology TK Electrical Engineering. Electronics. Nuclear Engineering Mohammed Abdo Al-Qershi, Osamah Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique |
| title | 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_short | 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 |
| topic | T Technology TK Electrical Engineering. Electronics. Nuclear Engineering |
| url | 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 |