Image Manipulation Detection Using Harris Corner and ANMS

  • Choudhary Shyam Prakash
  • Sushila Maheshkar
  • Vikas Maheshkar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)

Abstract

Due to the availability of media editing software, the authenticity and reliability of digital images are important. Region manipulation is a simple and effective method for digital image forgeries. Hence, the potential to identify the image manipulation is current research issue these days and copy–move forgery detection (CMFD) is a main domain in image authentication. In copy–move forgery, one region is simply copied and pasted over other region in the same image for manipulating the image. In this paper, we have proposed a method based on Harris corner and adaptive non-maximal Suppression (ANMS). Initially, the input image is taken, and then Harris corner detection algorithm is used to detect the interest points, and ANMS is adopted to control the number of Harris points in an image. This gives an appropriate number of interest points for different size of images and gives the assurance for finding the manipulated region in manageable time. For each extracted interest point, SIFT is used for calculating the descriptors. Now obtained descriptors are matched using the outlier rejection with nearest neighbour. Here RANSAC is used to find the best set of matches to identify the manipulated regions. Experimental results show the robustness against different transformation and post-processing operations.

Keywords

Copy–move forgery Image forensics SIFT descriptor ANMS Duplicate region detection 

References

  1. 1.
    Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., 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.
    Bo, X., Junwen, W., Guangjie, L., Yuewei, D.: Image copy-move forgery detection based on surf. In: 2010 international conference on Multimedia information networking and security (MINES), pp. 889–892. IEEE (2010)Google Scholar
  3. 3.
    Brown, M., Lowe, D.G.: Invariant features from interest point groups. In: BMVC, vol. 4 (2002)Google Scholar
  4. 4.
    Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74(1), 59–73 (2007)CrossRefGoogle Scholar
  5. 5.
    Chen, L., Lu, W., Ni, J., Sun, W., Huang, J.: Region duplication detection based on harris corner points and step sector statistics. J. Vis. Commun. Image Represent. 24(3), 244–254 (2013)CrossRefGoogle Scholar
  6. 6.
    Christlein, V., Riess, C., Jordan, J., Riess, C., 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.
    Fridrich, A.J., Soukal, B.D., Lukáš, A.J.: Detection of copy-move forgery in digital images. In: Proceedings of Digital Forensic Research Workshop. Citeseer (2003)Google Scholar
  8. 8.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference. vol. 15, pp. 10–5244. Citeseer (1988)Google Scholar
  9. 9.
    Huang, H., Guo, W., Zhang, Y.: Detection of copy-move forgery in digital images using sift algorithm. In: Computational Intelligence and Industrial Application, 2008. PACIIA’08. Pacific-Asia Workshop on. vol. 2, pp. 272–276. IEEE (2008)Google Scholar
  10. 10.
    Kang, X., Wei, S.: Identifying tampered regions using singular value decomposition in digital image forensics. In: 2008 International Conference on Computer Science and Software Engineering, vol. 3, pp. 926–930. IEEE (2008)Google Scholar
  11. 11.
    Li, G., Wu, Q., Tu, D., Sun, S.: A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 1750–1753. IEEE (2007)Google Scholar
  12. 12.
    Luo, W., Huang, J., Qiu, G.: Robust detection of region-duplication forgery in digital image. In: 18th International Conference on Pattern Recognition, 2006. ICPR 2006. vol. 4, pp. 746–749. IEEE (2006)Google Scholar
  13. 13.
    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
  14. 14.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  15. 15.
    Moravec, H.P.: Obstacle avoidance and navigation in the real world by a seeing robot rover. Technical report, DTIC Document (1980)Google Scholar
  16. 16.
    Pan, X., Lyu, S.: Detecting image region duplication using sift features. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 1706–1709. IEEE (2010)Google Scholar
  17. 17.
    Pan, X., Lyu, S.: Region duplication detection using image feature matching. IEEE Trans. Inf. Forensics Secur. 5(4), 857–867 (2010)CrossRefGoogle Scholar
  18. 18.
    Popescu, A., Farid, H.: Exposing digital forgeries by detecting duplicated image region [technical report]. 2004–515. Hanover, Department of Computer Science, Dartmouth College. USA p. 32 (2004)Google Scholar
  19. 19.
    Shivakumar, B., Baboo, L.D.S.S.: Detection of region duplication forgery in digital images using surf. IJCSI Int. J. Comput. Sci. Issues 8(4) (2011)Google Scholar
  20. 20.
    Silva, E., Carvalho, T., Ferreira, A., Rocha, A.: 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 (2015)CrossRefGoogle Scholar
  21. 21.
    Tralic, D., Zupancic, I., Grgic, S., Grgic, M.: CoMoFod new database for copy-move forgery detection. In: ELMAR, 2013 55th International Symposium, pp. 49–54. IEEE (2013)Google Scholar
  22. 22.
    Zhang, J., Feng, Z., Su, Y.: A new approach for detecting copy-move forgery in digital images. In: 11th IEEE Singapore International Conference on Communication Systems, 2008. ICCS 2008, pp. 362–366. IEEE (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Choudhary Shyam Prakash
    • 1
  • Sushila Maheshkar
    • 1
  • Vikas Maheshkar
    • 2
  1. 1.Indian Institute of Technology (Indian School of Mines)DhanbadIndia
  2. 2.Netaji Subhas Institute of TechnologyDelhiIndia

Personalised recommendations