An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal
Copy-move image forgery detection has become a significant research subject in multimedia forensics and security due to its widespread use and its hard detection. In this type of image forging, a region of the image is copied and pasted elsewhere in the same image. Keypoint-based forgery detection a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-11-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157819304707 |
| _version_ | 1818360025881509888 |
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| author | Aya Hegazi Ahmed Taha Mazen M. Selim |
| author_facet | Aya Hegazi Ahmed Taha Mazen M. Selim |
| author_sort | Aya Hegazi |
| collection | DOAJ |
| description | Copy-move image forgery detection has become a significant research subject in multimedia forensics and security due to its widespread use and its hard detection. In this type of image forging, a region of the image is copied and pasted elsewhere in the same image. Keypoint-based forgery detection approaches use local visual features to identify the duplicated regions. The performance of keypoint-based methods degrades in those cases when the duplicated regions are near to each other and when handling highly textured area. The clustering algorithm that mostly used in keypoint- based methods suffer from high complexity. In this paper, an improved approach for keypoint- based copy-move forgery detection is proposed. The proposed method is based on density-based clustering and Guaranteed Outlier Removal algorithm. Experimental results carried out on various benchmark datasets exhibit that the proposed method surpasses other similar state-of-the-art techniques under different challenging conditions, such as geometric attacks, post-processing attacks, and multiple cloning. |
| first_indexed | 2024-12-13T20:54:14Z |
| format | Article |
| id | doaj.art-38df9bbd45814aa58480e83b591d3715 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-12-13T20:54:14Z |
| publishDate | 2021-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-38df9bbd45814aa58480e83b591d37152022-12-21T23:31:47ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782021-11-0133910551063An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removalAya Hegazi0Ahmed Taha1Mazen M. Selim2Corresponding author.; Faculty of Computers & Informatics, Benha University, EgyptFaculty of Computers & Informatics, Benha University, EgyptFaculty of Computers & Informatics, Benha University, EgyptCopy-move image forgery detection has become a significant research subject in multimedia forensics and security due to its widespread use and its hard detection. In this type of image forging, a region of the image is copied and pasted elsewhere in the same image. Keypoint-based forgery detection approaches use local visual features to identify the duplicated regions. The performance of keypoint-based methods degrades in those cases when the duplicated regions are near to each other and when handling highly textured area. The clustering algorithm that mostly used in keypoint- based methods suffer from high complexity. In this paper, an improved approach for keypoint- based copy-move forgery detection is proposed. The proposed method is based on density-based clustering and Guaranteed Outlier Removal algorithm. Experimental results carried out on various benchmark datasets exhibit that the proposed method surpasses other similar state-of-the-art techniques under different challenging conditions, such as geometric attacks, post-processing attacks, and multiple cloning.http://www.sciencedirect.com/science/article/pii/S1319157819304707Copy-move detectionImage forensicsKeypoint-based methodsMultiple-copied matchingDBSCANGORE |
| spellingShingle | Aya Hegazi Ahmed Taha Mazen M. Selim An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal Journal of King Saud University: Computer and Information Sciences Copy-move detection Image forensics Keypoint-based methods Multiple-copied matching DBSCAN GORE |
| title | An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal |
| title_full | An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal |
| title_fullStr | An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal |
| title_full_unstemmed | An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal |
| title_short | An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal |
| title_sort | improved copy move forgery detection based on density based clustering and guaranteed outlier removal |
| topic | Copy-move detection Image forensics Keypoint-based methods Multiple-copied matching DBSCAN GORE |
| url | http://www.sciencedirect.com/science/article/pii/S1319157819304707 |
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