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

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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.

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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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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116029, People’s Republic of China

    Hong-ying Yang, Ying Niu, Li-xian Jiao, Yu-nan Liu & Xiang-yang Wang

  2. Jiangsu Engineering Center of Network Monitoring & School of Computer and Software, Nanjing University of Information Science & Technology (NUIST), Nanjing, 210044, China

    Zhi-li Zhou

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  1. Hong-ying Yang
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  2. Ying Niu
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  3. Li-xian Jiao
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  4. Yu-nan Liu
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  5. Xiang-yang Wang
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  6. Zhi-li Zhou
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Correspondence to Hong-ying Yang or Xiang-yang Wang.

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Yang, Hy., Niu, Y., Jiao, Lx. et al. Robust copy-move forgery detection based on multi-granularity Superpixels matching. Multimed Tools Appl 77, 13615–13641 (2018). https://doi.org/10.1007/s11042-017-4978-1

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  • DOI: https://doi.org/10.1007/s11042-017-4978-1

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