Cloning Localization Based on Feature Extraction and K-means Clustering

  • Areej S. Alfraih
  • Johann A. Briffa
  • Stephan Wesemeyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9023)


The field of image forensics is expanding rapidly. Many passive image tamper detection techniques have been presented. Some of these techniques use feature extraction methods for tamper detection and localization. This work is based on extracting Maximally Stable Extremal Regions (MSER) features for cloning detection, followed by k-means clustering for cloning localization. Then for comparison purposes, we implement the same approach using Speeded Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT). Experimental results show that we can detect and localize cloning in tampered images with an accuracy reaching 97 % using MSER features. The usability and efficacy of our approach is verified by comparing with recent state-of-the-art approaches.


Cloning localization MSER features SIFT SURF K-means clustering 


  1. 1.
    Amerini, I., Ballan, L., Caldelli, R., Bimbo, A.D., Serra, G.: A sift-based forensic method for copy-move attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6(3), 1099–1110 (2011)CrossRefGoogle Scholar
  2. 2.
    Amerini, I., Ballan, L., Caldelli, R., Bimbo, A.D., Tongo, L.D., Serra, G.: Copy-move forgery detection and localization by means of robust clustering with j-linkage. Signal Process. Image Commun. 28, 659–669 (2013)CrossRefGoogle Scholar
  3. 3.
    Bay, H., Tuytelaars, T., van Gool, L.: Surf: speed up robust features. In: Proceedings of Computer Vision-ECCV, pp. 404–417 (2006)Google Scholar
  4. 4.
    Shivakumar, B.L., Baboo, L.D.S.: Detection of region duplication forgery in digital images using surf. Int. J. Comput. Sci. Issues 8(4), 1 199–205 (2011)Google Scholar
  5. 5.
    Bo, X., Junwen, W., Guangjie, L., Yuewei, D.: Image copy-move forgery detection based on surf. In: International Conference on Multimedia Information Networking and Security (2010)Google Scholar
  6. 6.
    Christlein, V., Riess, C., Jordan, J., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)CrossRefGoogle Scholar
  7. 7.
    Davarzani, R., Yaghmaie, K., Mozaffari, S., Tapak, M.: Copy-move forgery detection using multiresolution local binary patterns. Forensic Sci. Int. 231, 61–72 (2013)CrossRefGoogle Scholar
  8. 8.
    Kimmel, R., Zhang, C., Bronstein, A., Bronstein, M.: Are MSER features really interesting? IEEE Trans. PAMI 33(11), 2316–2320 (2010)CrossRefGoogle Scholar
  9. 9.
    Li, L., Li, S., Zhu, H., Wu, X.: Detecting copy-move forgery under affine transforms for image forensics. Comput. Electr. Eng. 40(6), 1951–1956 (2013)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Lowe, D.G.: Distinctive image feature from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Kadir, T., Gool, L.V.: A comparison of affine region detectors. Int. J. Comput. Vis. 65, 43–72 (2005)CrossRefGoogle Scholar
  12. 12.
    Oh, I.-S., Lee, J., Majumder, A.: Multi-scale image segmentation using MSER. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part II. LNCS, vol. 8048, pp. 201–208. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  13. 13.
    Okade, M., Biswas, P.K.: Video stabilization using maximally stable extremal region features. Multimedia Tools Appl. 68(3), 947–968 (2012)CrossRefGoogle Scholar
  14. 14.
    Ryu, S.J., Kirchner, M., Lee, M.J., Lee, H.K.: Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans. Inf. Forensics Secur. 8(8), 1355–1370 (2013)CrossRefGoogle Scholar
  15. 15.
    Ryu, S.-J., Lee, M.-J., Lee, H.-K.: Detection of copy-rotate-move forgery using Zernike moments. In: Böhme, R., Fong, P.W.L., Safavi-Naini, R. (eds.) IH 2010. LNCS, vol. 6387, pp. 51–65. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  16. 16.
    YunJie, W., Yu, D., HaiBin, D., LinNa, Z.: Dual tree complex wavelet transform approach to copy-rotate-move forgery detection. Inf. Sci. 57(1), 1–12 (2014)Google Scholar
  17. 17.
    Zhao, J., Zhao, W.: Passive forensic for region duplication image forgery based on harris feature points and local binary patterns. Sci. World J. 2013, 1 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Areej S. Alfraih
    • 1
  • Johann A. Briffa
    • 1
  • Stephan Wesemeyer
    • 1
  1. 1.Department of ComputingUniversity of SurreyGuildfordUK

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