A Copy-Move Detection Algorithm Using Binary Gradient Contours

  • Andrey Kuznetsov
  • Vladislav Myasnikov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9730)


Nowadays copy-move attack is one of the most obvious ways of digital image forgery in order to hide the information contained in images. Copy-move process consists of copying the fragment from one place of an image, changing it and pasting it to another place of the same image. However, only a few existing studies reached high detection accuracy for a narrow range of transform parameters. In this paper, we propose a copy-move detection algorithm that uses features based on binary gradient contours that are robust to contrast enhancement, additive noise and JPEG compression. The proposed solution showed high detection accuracy and the results are supported by conducted experiments for wide ranges of transform parameters. A comparison of features based on binary gradient contours and based on various forms of local binary patterns showed a significant 20–30 % difference in detection accuracy, corresponding to an improvement with the proposed solution.


Copy-move detection Transformed duplicate Forgery Local binary pattern Binary gradient contours k-d tree 



This work was financially supported by the Russian Scientific Foundation (RSF), grant no. 14-31-00014 “Establishment of a Laboratory of Advanced Technology for Earth Remote Sensing”.


  1. 1.
    The Top 20 Valuable Facebook Statistics.
  2. 2.
    Christlein, V., Riess, C., Jordan, J., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensic Secur. 7(6), 1841–1854 (2012)CrossRefGoogle Scholar
  3. 3.
    Mahdian, B., Saic, S.: Detection of copy-move forgery using a method based on blur moment invariants. Forensic Sci. Int. 171(2), 180–189 (2007)CrossRefGoogle Scholar
  4. 4.
    Kang, X., Wei, S.: Identifying tampered regions using singular value decomposition in digital image forensics. In: International Conference on Computer Science and Software Engineering, vol. 3, pp. 926–930. IEEE Press, New York (2008)Google Scholar
  5. 5.
    Huang, H., Guo, W., Zhang, Y.: Detection of copy-move forgery in digital images using SIFT algorithm. In: Pacific-Asia Workshop on Computational Intelligence and Industrial Application 2008,vol. 2, pp. 272–276 (2008)Google Scholar
  6. 6.
    Shivakumar, B.L., Baboo, S.: Detection of region duplication forgery in digital images using SURF. Int. J. Comput. Sci. Issues 8(4), 199–205 (2011)Google Scholar
  7. 7.
    Fridrich, J., Soukal, D., Lukas, J.: Detection of copy-move forgery in digital images.
  8. 8.
    Bayram, S., Sencar, H., Memon, H.: An efficient and robust method for detecting copy-move forgery. In: IEEE International Conference on Acoustics, Speech, and Signal Processing 2009, pp. 1053–1056 (2009)Google Scholar
  9. 9.
    Popescu, A., Farid, H.: Exposing digital forgeries by detecting duplicated image regions.
  10. 10.
    Kang, X., Wei, S.: Identifying tampered regions using singular value decomposition in digital image forensics. In: International Conference on Computer Science and Software Engineering 2008, vol. 3, pp. 926–930 (2008)Google Scholar
  11. 11.
    Ryu, S.-J., Lee, M.-J., Lee, H.-K.: Detection of copy-rotate-move forgery using Zernike moments. In: Böhme, R., Fong, P.W., Safavi-Naini, R. (eds.) IH 2010. LNCS, vol. 6387, pp. 51–65. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Vladimirovich, K.A., Valerievich, M.V.: A fast plain copy-move detection algorithm based on structural pattern and 2D Rabin-Karp rolling hash. In: Campilho, A., Kamel, M. (eds.) ICIAR 2014, Part I. LNCS, vol. 8814, pp. 461–468. Springer, Heidelberg (2014)Google Scholar
  13. 13.
    Li, L., Li, S., Zhu, H.: An efficient scheme for detection copy-move forged images by local binary patterns. J. Inf. Hiding Multimed. Sig. Process. 4(1), 46–56 (2013)Google Scholar
  14. 14.
    Davarzani, R., Yaghmaie, K., Mozaffari, S., Tapak, M.: Copy-move forgery detection using multi-resolution local binary patterns. Forensic Sci. Int. 231(1–3), 61–72 (2013)CrossRefGoogle Scholar
  15. 15.
    Ren, J., Jiang, X., Yuan, J.: Noise-resistant local binary pattern with an embedded error-correction mechanism. IEEE Trans. Image Process. 22(10), 4049–4060 (2013)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Fernández, A., Álvarez, M.X., Bianconi, F.: Image classification with binary gradient contours. Opt. Lasers Eng. 49(9–10), 1177–1184 (2011)CrossRefGoogle Scholar
  17. 17.
    Wang, L., He, D.-C.: Texture classification using texture spectrum. Pattern Recogn. 23(8), 905–910 (1990)CrossRefGoogle Scholar
  18. 18.
    Ojala, T., Pietikinen, M., Menp, T.: Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  19. 19.
    Myasnikov, V.: A local order transform of digital images. Comput. Opt. 39(3), 397–405 (2015). (in Russian)Google Scholar
  20. 20.
    Arasteh, S., Hung, C.-C.: Color and texture image segmentation using uniform local binary patterns. Mach. Graph. Vis. 15(3–4), 265–274 (2006)Google Scholar
  21. 21.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Guo, Z.H., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Samara National Research University (SNRU)SamaraRussia
  2. 2.Image Processing Systems Institute of the Russian Academy of Sciences (IPSI RAS)SamaraRussia

Personalised recommendations