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


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.


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



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.


  1. 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):659–669CrossRefGoogle Scholar
  2. 2.
    Ardizzone E, Bruno A, Mazzola G (2015) Copy-move forgery detection by matching triangles of keypoints. IEEE Trans Inf Forensics Secur 10(10):2084–2094CrossRefGoogle Scholar
  3. 3.
    Bi X, Pun CM, Yuan XC (2016) Multi-level dense descriptor and hierarchical feature matching for copy-move forgery detection. Inf Sci 345:226–242CrossRefGoogle Scholar
  4. 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):244–254CrossRefGoogle Scholar
  5. 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. 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):1841–1854CrossRefGoogle Scholar
  7. 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):1450–1464CrossRefGoogle Scholar
  8. 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, 5312–5316Google Scholar
  9. 9.
    Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy–move forgery detection. IEEE Trans Inf Forensics Secur 10(11):2284–2297CrossRefGoogle Scholar
  10. 10.
    Geusebroek JM, Van den Boomgaard R, Smeulders AWM et al (2001) Color invariance. IEEE Trans Pattern Anal Mach Intell 23(12):1338–1350CrossRefGoogle Scholar
  11. 11.
    Jiang YJ (2011) Exponent moments and its application in pattern recognition. Beijing University of Posts and Telecommunications, BeijingGoogle Scholar
  12. 12.
    Lee JC (2015) Copy-move image forgery detection based on Gabor magnitude. J Vis Commun Image Represent 31:320–334CrossRefGoogle Scholar
  13. 13.
    Lee JC, Chang CP, Chen WK (2015) Detection of copy–move image forgery using histogram of orientated gradients. Inf Sci 321:250–262CrossRefGoogle Scholar
  14. 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):507–518CrossRefGoogle Scholar
  15. 15.
    Li Y, Wang S, Tian Q et al (2015) A survey of recent advances in visual feature detection. Neurocomputing 149:736–751CrossRefGoogle Scholar
  16. 16.
    Lim WQ (2010) The discrete shearlet transform: a new directional transform and compactly supported shearlet frames. IEEE Trans Image Process 19(5):1166–1180MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Liu M, Tuzel O, Ramalingam S, Chellappa R (2011) Entropy rate superpixel segmentation. Proceedings of the IEEE Conference on Computer Vision and PatternGoogle Scholar
  18. 18.
    Mainali P, Lafruit G, Yang Q et al (2013) SIFER: scale-invariant feature detector with error resilience. Int J Comput Vis 104(2):172–197CrossRefzbMATHGoogle Scholar
  19. 19.
    Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–867CrossRefGoogle Scholar
  20. 20.
    Pandey R, Singh S, Shukla K (2016) Passive forensics in image and video using noise features: a review. Digit Investig 19:1–28CrossRefGoogle Scholar
  21. 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):1705–1716CrossRefGoogle Scholar
  22. 22.
    Qureshi M, Deriche M (2015) A bibliography of pixel-based blind image forgery detection techniques. Signal Process Image Commun 39:46–74CrossRefGoogle Scholar
  23. 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):1355–1370CrossRefGoogle Scholar
  24. 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 29:16–32CrossRefGoogle Scholar
  25. 25.
    Wang X, Niu P, Yang H et al (2014) A new robust color image watermarking using local quaternion exponent moments. Inf Sci 277:731–754CrossRefGoogle Scholar
  26. 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. 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):559–562CrossRefGoogle Scholar
  28. 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):2594–2608CrossRefGoogle Scholar
  29. 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. 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):1159–1176CrossRefGoogle Scholar
  31. 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):2499–2512CrossRefGoogle Scholar
  32. 32.
    Zhang R, Wei F, Li B (2014) E2LSH based multiple kernel approach for object detection. Neurocomputing 124:105–110CrossRefGoogle Scholar
  33. 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):1531–1540Google Scholar
  34. 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):48–63CrossRefGoogle Scholar
  35. 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):717–729CrossRefGoogle Scholar

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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