Detection of Copy-Move Forgery in Flat Region Based on Feature Enhancement

  • Weiwei Zhang
  • Zhenghong Yang
  • Shaozhang NiuEmail author
  • Junbin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10082)


A new Feature Enhancement method based on SURF is proposed for Copy-Move Forgery Detection. The main difference from the traditional methods is that Contrast Limited Adaptive Histogram Equalization is proposed as a preprocessing stage in images. SURF is used to extract keypoints from the preprocessed image. Even in flat regions, the method can also extract enough keypoints. In the matching stage, g2NN matching skill is used which can also detect multiple forgeries. The experimental results show that the proposed method performs better than the state-of-the-art algorithms on the public database.


Copy-move forgery detection Feature enhancement method CLAHE algorithm Flat regions 



This work was supported by National Natural Science Foundation of China (No. 61370195, U1536121).


  1. 1.
    Fridrich, B.A.J., Soukal, B.D., Lukáš, A.J.: Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop (2003)Google Scholar
  2. 2.
    Popescu, A.C., Farid, H.: Exposing digital forgeries by detecting duplicated image regions. In: Computer Science Dartmouth College Private Ivy League Research University, 646 (2004)Google Scholar
  3. 3.
    Mahdian, B., Saic, S.: Detection of copy–move forgery using a method based on blur moment invariants. Forensic Sci. Int. 171(2–3), 180–189 (2007)CrossRefGoogle Scholar
  4. 4.
    Li, G., Wu, Q., Tu, D., et al.: A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. In: IEEE International Conference on Multimedia and Expo, ICME 2007, 2–5 July 2007, Beijing, pp. 1750–1753 (2007)Google Scholar
  5. 5.
    Bayram, S., Sencar, H.T., Memon, N.: An efficient and robust method for detecting copy-move forgery. In: IEEE International Conference on Acoustics, pp. 1053–1056 (2009)Google Scholar
  6. 6.
    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). doi: 10.1007/978-3-642-16435-4_5 CrossRefGoogle Scholar
  7. 7.
    Kakar, P., Sudha, N.: Exposing postprocessed copy–paste forgeries through transform-invariant features. IEEE Trans. Inf. Forensics Secur. 7(3), 1018–1028 (2012)CrossRefGoogle Scholar
  8. 8.
    Li, L., Li, S., Zhu, H., et al.: An efficient scheme for detecting copy-move forged images by local binary patterns. J. Inf. Hiding Multimed. Signal Process. 4, 46–56 (2013)Google Scholar
  9. 9.
    Mahmood, T., Nawaz, T., Ashraf, R., et al.: A survey on block based copy move image forgery detection techniques. In: International Conference on Emerging Technologies. IEEE (2015)Google Scholar
  10. 10.
    Pan, X., Lyu, S.: Region duplication detection using image feature matching. IEEE Trans. Inf. Forensics Secur. 5(4), 857–867 (2010)CrossRefGoogle Scholar
  11. 11.
    Amerini, I., Ballan, L., Caldelli, R., et al.: 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
  12. 12.
    Amerini, I., Ballan, L., Caldelli, R., et al.: Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Process. Image Commun. 28(6), 659–669 (2013)CrossRefGoogle Scholar
  13. 13.
    Bo, X., Wang, J., Liu, G., et al.: Image copy-move forgery detection based on SURF. In: International Conference on Multimedia Information Networking & Security. pp. 889–892 (2010)Google Scholar
  14. 14.
    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
  15. 15.
    Pisano, E.D., Zong, S., Hemminger, B.M., DeLuca, M., Johnston, R.E., Muller, K., Braeuning, M.P., Pizer, S.M.: Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J. Digit. Imaging 11, 193–200 (1998)CrossRefGoogle Scholar
  16. 16.
    Christlein, V., Riess, C., Jordan, J., et al.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)CrossRefGoogle Scholar
  17. 17.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Readings in Computer Vision: Issues, Problems, Principles, and Paradigms, pp. 726–740. Morgan Kaufmann Publishers Inc., San Francisco (1987)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Weiwei Zhang
    • 1
  • Zhenghong Yang
    • 1
  • Shaozhang Niu
    • 2
    Email author
  • Junbin Wang
    • 1
  1. 1.School of ScienceChina Agricultural UniversityBeijingChina
  2. 2.Beijing Key Lab of Intelligent Telecommunication Software and MultimediaBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations