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Multimedia Tools and Applications

, Volume 77, Issue 11, pp 13615–13641 | Cite as

Robust copy-move forgery detection based on multi-granularity Superpixels matching

  • Hong-ying Yang
  • Ying Niu
  • Li-xian Jiao
  • Yu-nan Liu
  • Xiang-yang Wang
  • Zhi-li Zhou
Article
  • 223 Downloads

Abstract

In this paper, we propose a new multi-granularity superpixels matching based algorithm for the accurate detection and localization of copy-move forgeries, which integrated the advantages of keypoint-based and block-based forgery detection approaches. Firstly, we divide the original tempted image into non-overlapping and irregular coarse-granularity superpixels, and the stable image keypoints are extracted from each coarse-granularity superpixel. Secondly, the superpixel features, which is quaternion exponent moments magnitudes, are extracted from each coarse-granularity superpixel, and we find the matching coarse-granularity superpixels (suspected forgery region pairs) rapidly using the Exact Euclidean Locality Sensitive Hashing (E2LSH). Thirdly, the suspected forgery region pairs are further segmented into fine-granularity superpixels, and the matching keypoints within the suspected forgery region pairs are replaced with the fine-granularity superpixels. Finally, the neighboring fine-granularity superpixels are merged, and we obtain the detected forgery regions through morphological operation. Compared with the state-of-the-art approaches, extensive experimental results, conducted on the public databases available online, demonstrate the good performance of our proposed algorithm even under a variety of challenging conditions.

Keywords

Copy-move forgery detection Multi-granularity superpixel Quaternion exponent moments SIFER E2LSH 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 61472171 & 61272416, the Natural Science Foundation of Liaoning Province of China under Grant No. 201602463, A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Computer and Information TechnologyLiaoning Normal UniversityDalianPeople’s Republic of China
  2. 2.Jiangsu Engineering Center of Network Monitoring & School of Computer and SoftwareNanjing University of Information Science & Technology (NUIST)NanjingChina

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