A comparative analysis on three duplication elements in copy-move forgery using patchmatch-based detection method
Image forgery is the alteration of a digital image to hide some of the important and useful information. Copy-move forgery (CMF) is one of the most difficult to detect because the copied part of the image has the same characteristics as the original image. Most of the existing datasets only highligh...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40337/1/A%20comparative%20analysis%20on%20three%20duplication%20elements.pdf http://umpir.ump.edu.my/id/eprint/40337/2/A%20comparative%20analysis%20on%20three%20duplication%20elements%20in%20copy-move%20forgery%20using%20patchmatch-based%20detection%20method_ABS.pdf http://umpir.ump.edu.my/id/eprint/40337/ https://doi.org/10.1109/ICSECS58457.2023.10256380 |
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| Summary: | Image forgery is the alteration of a digital image to hide some of the important and useful information. Copy-move forgery (CMF) is one of the most difficult to detect because the copied part of the image has the same characteristics as the original image. Most of the existing datasets only highlight additional attacks in the copied part. Since there are no categories of duplication elements in the datasets, this research analyzed three categories of duplication elements in CMF which are animals, food and non-living things using DEFACTO and CoMo3Dataset. The analysis is performed on PatchMatch-based detection method and the results show that the method able to maintain at least 83% for all duplication elements in both DEFACTO and CoMo3Dataset. Furthermore, the method is able to detect a minimum 92% score for the food category in both datasets. |
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