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Link to original content: https://doi.org/10.1007/s11042-017-4978-1
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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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References

  1. Amerini I, Ballan L, Caldelli R et al (2013) Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Process Image Commun 28(6):659–669

    Article  Google Scholar 

  2. Ardizzone E, Bruno A, Mazzola G (2015) Copy-move forgery detection by matching triangles of keypoints. IEEE Trans Inf Forensics Secur 10(10):2084–2094

    Article  Google Scholar 

  3. Bi X, Pun CM, Yuan XC (2016) Multi-level dense descriptor and hierarchical feature matching for copy-move forgery detection. Inf Sci 345:226–242

    Article  Google Scholar 

  4. Chen L, Lu W, Ni J et al (2013) Region duplication detection based on Harris corner points and step sector statistics. J Vis Commun Image Represent 24(3):244–254

    Article  Google Scholar 

  5. Chen B, Yang J, Jeon B, Zhang X (2017) Kernel quaternion principal component analysis and its application in RGB-D object recognition. Neurocomputing. doi:10.1016/j.neucom.2017.05.047

  6. Christlein V, Riess C, Jordan J et al (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensics Secur 7(6):1841–1854

    Article  Google Scholar 

  7. Costanzo A, Amerini I, Caldelli R et al (2014) Forensic analysis of SIFT keypoint removal and injection. IEEE Trans Inf Forensics Secur 9(9):1450–1464

    Article  Google Scholar 

  8. Cozzolino D, Poggi G, Verdoliva L (2014) Copy-move forgery detection based on patchmatch. 2014 I.E. International Conference on Image Processing (ICIP), Paris, France, 5312–5316

  9. Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy–move forgery detection. IEEE Trans Inf Forensics Secur 10(11):2284–2297

    Article  Google Scholar 

  10. Geusebroek JM, Van den Boomgaard R, Smeulders AWM et al (2001) Color invariance. IEEE Trans Pattern Anal Mach Intell 23(12):1338–1350

    Article  Google Scholar 

  11. Jiang YJ (2011) Exponent moments and its application in pattern recognition. Beijing University of Posts and Telecommunications, Beijing

    Google Scholar 

  12. Lee JC (2015) Copy-move image forgery detection based on Gabor magnitude. J Vis Commun Image Represent 31:320–334

    Article  Google Scholar 

  13. Lee JC, Chang CP, Chen WK (2015) Detection of copy–move image forgery using histogram of orientated gradients. Inf Sci 321:250–262

    Article  Google Scholar 

  14. Li J, Li X, Yang B et al (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518

    Article  Google Scholar 

  15. Li Y, Wang S, Tian Q et al (2015) A survey of recent advances in visual feature detection. Neurocomputing 149:736–751

    Article  Google Scholar 

  16. Lim WQ (2010) The discrete shearlet transform: a new directional transform and compactly supported shearlet frames. IEEE Trans Image Process 19(5):1166–1180

    Article  MathSciNet  MATH  Google Scholar 

  17. Liu M, Tuzel O, Ramalingam S, Chellappa R (2011) Entropy rate superpixel segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern

  18. Mainali P, Lafruit G, Yang Q et al (2013) SIFER: scale-invariant feature detector with error resilience. Int J Comput Vis 104(2):172–197

    Article  MATH  Google Scholar 

  19. Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–867

    Article  Google Scholar 

  20. Pandey R, Singh S, Shukla K (2016) Passive forensics in image and video using noise features: a review. Digit Investig 19:1–28

    Article  Google Scholar 

  21. Pun CM, Yuan XC, Bi XL (2015) Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Trans Inf Forensics Secur 10(8):1705–1716

    Article  Google Scholar 

  22. Qureshi M, Deriche M (2015) A bibliography of pixel-based blind image forgery detection techniques. Signal Process Image Commun 39:46–74

    Article  Google Scholar 

  23. Ryu SJ, Kirchner M, Lee MJ et al (2013) Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans Inf Forensics Secur 8(8):1355–1370

    Article  Google Scholar 

  24. Silva E, Carvalho T, Ferreira A et al (2015) Going deeper into copy-move forgery detection: exploring image telltales via multi-scale analysis and voting processes. J Vis Commun Image Represent 29:16–32

    Article  Google Scholar 

  25. Wang X, Niu P, Yang H et al (2014) A new robust color image watermarking using local quaternion exponent moments. Inf Sci 277:731–754

    Article  Google Scholar 

  26. Wang J, Li T, Shi Y-Q, Lian S, Ye J (2016) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed Tool Appl. doi:10.1007/s11042-016-4153-0

  27. Wu Q, Wang S, Zhang X (2011) Log-polar based scheme for revealing duplicated regions in digital images. IEEE Signal Process Lett 18(10):559–562

    Article  Google Scholar 

  28. Xia Z, Wang X, Zhang L, Qin Z, Sun X, Ren K (2016) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Forensics Secur 11(11):2594–2608

    Article  Google Scholar 

  29. Xiang-yang W, Yu-nan L, Huan X, Pei W, Hong-ying Y Robust copy-move forgery detection using quaternion exponent moments. Pattern Anal Applic. doi:10.1007/s10044-016-0588-1

  30. Yu L, Han Q, Niu X (2016) Feature point-based copy-move forgery detection: covering the non-textured areas. Multimedia Tools Appl 75(2):1159–1176

    Article  Google Scholar 

  31. Zandi M, Mahmoudi-Aznaveh A, Talebpour A (2016) Iterative copy-move forgery detection based on a new interest point detector. IEEE Trans Inf Forensics Secur 11(11):2499–2512

    Article  Google Scholar 

  32. Zhang R, Wei F, Li B (2014) E2LSH based multiple kernel approach for object detection. Neurocomputing 124:105–110

    Article  Google Scholar 

  33. Zhili Z, Ching-Nung Y, Xingming S et al (2016) Effective and efficient image copy detection with resistance to arbitrary rotation. IEICE Trans Inf Syst 99(6):1531–1540

    Google Scholar 

  34. Zhou Z, Wang Y, Wu Q et al (2017) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensics Secur 12(1):48–63

    Article  Google Scholar 

  35. Zong T, Xiang Y, Natgunanathan I et al (2015) Robust histogram shape-based method for image watermarking. IEEE Trans Circuits Syst Video Technol 25(5):717–729

    Article  Google Scholar 

Download references

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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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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