Seam carving based image resizing detection using hybrid features
Detection of seam carving-based digital image resizing is a challenging task in image processing field since the method investigates the images on hand semantically. Resizing with seam carving is realized by inserting or removing relatively unimportant pixel paths to/from the images and so the chang...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2017-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/280289 |
| _version_ | 1797207752719728640 |
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| author | Zehra Karapinar Senturk Devrim Akgun |
| author_facet | Zehra Karapinar Senturk Devrim Akgun |
| author_sort | Zehra Karapinar Senturk |
| collection | DOAJ |
| description | Detection of seam carving-based digital image resizing is a challenging task in image processing field since the method investigates the images on hand semantically. Resizing with seam carving is realized by inserting or removing relatively unimportant pixel paths to/from the images and so the changes in image content are mostly unnoticeable. Local Binary Patterns (LBP), a visual descriptor, unearths local changes in image texture. Therefore, using LBP transform of the images besides intensity values contributes to the detection ratio. In this paper, we proposed a hybrid detection mechanism for more accurate seam carving detection especially in low scaling ratios. We extracted LBP-based and non-LBP based features and trained a Support Vector Machine (SVM) with sixty features. We achieved approximately 9 % improvement in low detection ratios. The experimental results show that more satisfactory detection ratios can be obtained by the proposed hybrid approach. |
| first_indexed | 2024-04-24T09:27:54Z |
| format | Article |
| id | doaj.art-831f35ff1a734a57b4033f495c23fd2d |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:27:54Z |
| publishDate | 2017-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-831f35ff1a734a57b4033f495c23fd2d2024-04-15T14:28:11ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392017-01-012461825183210.17559/TV-20160804121351Seam carving based image resizing detection using hybrid featuresZehra Karapinar Senturk0Devrim Akgun1Duzce University, Faculty of Engineering, Department of Computer Engineering, Konuralp Campus, 81620, Duzce, TurkeySakarya University, Faculty of Computer and Information Sciences, Computer Engineering Department, Esentepe Campus, 54187, Sakarya, TurkeyDetection of seam carving-based digital image resizing is a challenging task in image processing field since the method investigates the images on hand semantically. Resizing with seam carving is realized by inserting or removing relatively unimportant pixel paths to/from the images and so the changes in image content are mostly unnoticeable. Local Binary Patterns (LBP), a visual descriptor, unearths local changes in image texture. Therefore, using LBP transform of the images besides intensity values contributes to the detection ratio. In this paper, we proposed a hybrid detection mechanism for more accurate seam carving detection especially in low scaling ratios. We extracted LBP-based and non-LBP based features and trained a Support Vector Machine (SVM) with sixty features. We achieved approximately 9 % improvement in low detection ratios. The experimental results show that more satisfactory detection ratios can be obtained by the proposed hybrid approach.https://hrcak.srce.hr/file/280289forgery detectionLocal Binary Patternsseam carvingSupport Vector Machines |
| spellingShingle | Zehra Karapinar Senturk Devrim Akgun Seam carving based image resizing detection using hybrid features Tehnički Vjesnik forgery detection Local Binary Patterns seam carving Support Vector Machines |
| title | Seam carving based image resizing detection using hybrid features |
| title_full | Seam carving based image resizing detection using hybrid features |
| title_fullStr | Seam carving based image resizing detection using hybrid features |
| title_full_unstemmed | Seam carving based image resizing detection using hybrid features |
| title_short | Seam carving based image resizing detection using hybrid features |
| title_sort | seam carving based image resizing detection using hybrid features |
| topic | forgery detection Local Binary Patterns seam carving Support Vector Machines |
| url | https://hrcak.srce.hr/file/280289 |
| work_keys_str_mv | AT zehrakarapinarsenturk seamcarvingbasedimageresizingdetectionusinghybridfeatures AT devrimakgun seamcarvingbasedimageresizingdetectionusinghybridfeatures |