Segmentation-Based Image Copy-Move Forgery Detection Scheme
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- 18 December 2014
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Information Forensics and Security
- Vol. 10 (3), 507-518
- https://doi.org/10.1109/tifs.2014.2381872
Abstract
In this paper, we propose a scheme to detect the copy-move forgery in an image, mainly by extracting the keypoints for comparison. The main difference to the traditional methods is that the proposed scheme first segments the test image into semantically independent patches prior to keypoint extraction. As a result, the copy-move regions can be detected by matching between these patches. The matching process consists of two stages. In the first stage, we find the suspicious pairs of patches that may contain copy-move forgery regions, and we roughly estimate an affine transform matrix. In the second stage, an Expectation-Maximization-based algorithm is designed to refine the estimated matrix and to confirm the existence of copy-move forgery. Experimental results prove the good performance of the proposed scheme via comparing it with the state-of-the-art schemes on the public databases.Keywords
Funding Information
- Jiangsu Basic Research Programs-Natural Science Foundation (BK20141006, BK20131004)
- Natural Science Foundation of the Universities in Jiangsu Province (14KJB520024)
- NSFC (61173142, 61232016, 61379151, 61300238)
- Startup Foundation for Introducing Talent of NUIST (2012x053)
- Priority Academic Program Development of Jiangsu Higer Education Institutions
- Suzhou Science Project-Applied Basic Research (SYG201315)
- PAPD fund
This publication has 30 references indexed in Scilit:
- Digital Image Forgery Detection Using JPEG Features and Local Noise DiscrepanciesThe Scientific World Journal, 2014
- SLIC Superpixels Compared to State-of-the-Art Superpixel MethodsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
- A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation RecoveryIEEE Transactions on Information Forensics and Security, 2011
- Copy-move forgery detection via texture descriptionPublished by Association for Computing Machinery (ACM) ,2010
- Region Duplication Detection Using Image Feature MatchingIEEE Transactions on Information Forensics and Security, 2010
- Contour Detection and Hierarchical Image SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2010
- Image Copy-Move Forgery Detection Based on SURFPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Detection of Copy-Move Forgery in Digital Images Using SIFT AlgorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- A Comparison of Affine Region DetectorsInternational Journal of Computer Vision, 2005
- Learning to detect natural image boundaries using local brightness, color, and texture cuesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2004