LogIn / RegistrationArticles for sale
- Head office address: Moscow, 123317, Moscow City, 8th Floor, Presnenskaya Embankment, 6, bldg. 2
- article@123mi.ru
- Сортировка:
- по горящим
- по цене
- по дате
#5491. A Novel Copy-Move Forgery Detection Algorithm via Feature Label Matching and Hierarchical Segmentation Filtering
| August 2026 | publication date |
| Proposal available till | 20-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Library and Information Sciences; Media Technology; Management Science and Operations Research; Information Systems; Computer Science Applications; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №5491.1   | Contract №5491.2 | Contract №5491.3 | Contract №5491.4 |
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
Recently, some copy-move forgeries have made use of homogeneous region(s) in an image with the large-scaling attack(s) to highlight or cover the target objects, which is easy to implement but difficult to detect. Unfortunately, existing Copy-Move Forgery Detection (CMFD) methods fail to detect such kinds of forgeries because they are incapable of extracting a sufficient number of effective keypoints in the homogeneous region(s), leading to inaccuracy and inefficiency in detection. In this work, a new CMFD scheme is proposed: 1) An improved SIFT structure with inherent scaling invariance is designed to enhance the capability of extracting effective keypoints in the homogeneous region. 2). The enhancement of massive keypoints extraction in the homogeneous region incurs a heavy computational burden in feature matching (Note that this is a common issue in all CMFD methods). For this reason, a new Feature Label Matching (FLM) method is proposed to break down the massive keypoints into different small label groups, each of which contains only a small number of keypoints, for significantly improved matching effectiveness and efficiency. 3) Identifying true keypoints for matching is a critical issue for performance. In our work, the Hierarchical Segmentation Filtering (HSF) algorithm is newly proposed to filter out suspicious outliers, based on the statistics on the coarse-to-fine hierarchical segmentations. 4) Finally, the fusion of the coarse-to-fine hierarchical segmentation maps fills the forgery regions precisely. In our experiments, the proposed scheme achieves excellent detection performance under various attacks, especially for the homogeneous region(s) detection under large-scaling attack(s).
Keywords:
Copy-move forgery detection (CMFD); Feature Label Matching; Hierarchical Segmentation Filtering; Improved SIFT structure
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
Recently, some copy-move forgeries have made use of homogeneous region(s) in an image with the large-scaling attack(s) to highlight or cover the target objects, which is easy to implement but difficult to detect. Unfortunately, existing Copy-Move Forgery Detection (CMFD) methods fail to detect such kinds of forgeries because they are incapable of extracting a sufficient number of effective keypoints in the homogeneous region(s), leading to inaccuracy and inefficiency in detection. In this work, a new CMFD scheme is proposed: 1) An improved SIFT structure with inherent scaling invariance is designed to enhance the capability of extracting effective keypoints in the homogeneous region. 2). The enhancement of massive keypoints extraction in the homogeneous region incurs a heavy computational burden in feature matching (Note that this is a common issue in all CMFD methods). For this reason, a new Feature Label Matching (FLM) method is proposed to break down the massive keypoints into different small label groups, each of which contains only a small number of keypoints, for significantly improved matching effectiveness and efficiency. 3) Identifying true keypoints for matching is a critical issue for performance. In our work, the Hierarchical Segmentation Filtering (HSF) algorithm is newly proposed to filter out suspicious outliers, based on the statistics on the coarse-to-fine hierarchical segmentations. 4) Finally, the fusion of the coarse-to-fine hierarchical segmentation maps fills the forgery regions precisely. In our experiments, the proposed scheme achieves excellent detection performance under various attacks, especially for the homogeneous region(s) detection under large-scaling attack(s).
Keywords:
Copy-move forgery detection (CMFD); Feature Label Matching; Hierarchical Segmentation Filtering; Improved SIFT structure
Contacts :
