Copy-Move Forgery Detection Using on Locality Sensitive Hashing and k-means Clustering

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 376)

Abstract

Digital images are a main source of information in our modern digital era. However, the easiness of manipulating digital images using simple user-friendly software makes the credibility of images questionable. Copy-Move is one of the most common image forgery types, where a region of an image is copied and pasted into another location of the same image. Such a forgery is simple to achieve but hard to be detected as the pasted region shares the same characteristics with the image. Although plenty of algorithms have been proposed to tackle the copy-move detection problem, a fast and reliable copy-move detection algorithm is not achieved yet. In this paper, a new matching method is proposed which can reduce the detection time and enhance the accuracy of detection as well. Such enhancement is done by clustering image blocks into clusters, and searching for identical blocks within each cluster instead of all image blocks. For that purpose, k-means clustering is used to cluster the image blocks then Locality Sensitive Hashing (LSH) method is used to match the blocks based on Zernike moments. The experimental results shows that the processing time has been reduced to 10% and the detection accuracy has been enhanced as well.

Keywords

Digital image forgery Copy-move Locality Sensitive Hashing Zernike moments K-means clustering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mahdian, B., Saic, S.: A bibliography on blind methods for identifying image forgery. Signal Process. Image Commun. 25(6), 389–399 (2010)CrossRefGoogle Scholar
  2. 2.
    Redi, J.A., Taktak, W., Dugelay, J.L.: Digital image forensics: A booklet for beginners. Multimed. Tools Appl. 51(1), 133–162 (2011)CrossRefGoogle Scholar
  3. 3.
    Al-Qershi, O.M., Khoo, B.E.: Passive detection of copy-move forgery in digital images: state-of-the-art. Forensic Sci. Int. 231(1–3), 284–295 (2013)CrossRefGoogle Scholar
  4. 4.
    Fridrich, J., Soukal, D., Lukáš, J.: Detection of copy-move forgery in digital images. In: Proceedings of DFRWS 2003, Cleveland, OH, USA (2003)Google Scholar
  5. 5.
    Bravo-Solorio, S., Nandi, A.K.: Automated detection and localisation of duplicated regions affected by reflection, rotation and scaling in image forensics. Signal Processing 91(8), 1759–1770 (2011)CrossRefMATHGoogle Scholar
  6. 6.
    Al-Qershi, O.M., Khoo, B.E.: Enhanced matching method for copy-move forgery detection by means of zernike moments. In: LNCS, vol. 9023, pp. 485–497. Springer, Heidelberg (2014)Google Scholar
  7. 7.
    Lynch, G., Shih, F.Y., Liao, H.-Y.M.: An efficient expanding block algorithm for image copy-move forgery detection. Inf. Sci. (Ny) 239, 253–265 (2013)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Christlein, V., Riess, C., Angelopoulou, E.: A study on features for the detection of copy-move forgeries. In: Proc. of Information Security Solutions Europe, pp. 105–116 ( 2010)Google Scholar
  10. 10.
    Langille, A., Gong, M.: An efficient match-based duplication detection algorithm. In: Third Canadian Conference on Computer and Robot Vision, CRV 2006, vol. 2006, pp. 64–71 (2006)Google Scholar
  11. 11.
    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
  12. 12.
    Li, Y.: Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Sci. Int. 224(1–3), 59–67 (2013)CrossRefGoogle Scholar
  13. 13.
    Luo, W., Huang, J., Qiu, G., Weiqi, L., Jiwu, H., Guoping, Q.: Robust detection of region-duplication forgery in digital image. In: Proceedings of the 18th International Conference on Pattern Recognition, vol. 4, pp. 746–749 (2006)Google Scholar
  14. 14.
    Ardizzone, E., Bruno, A., Mazzola, G.: Copy-move forgery detection via texture description. In: MiFor 2010 - Proceedings of the 2010 ACM Workshop on Multimedia in Forensics, Security and Intelligence, Co-located with ACM Multimedia 2010, pp. 59–64 (2010)Google Scholar
  15. 15.
    Hu, J., Zhang, H., Gao, Q., Huang, H.: An improved lexicographical sort algorithm of copy-move forgery detection. In: Proceedings - 2nd International Conference on Networking and Distributed Computing, ICNDC 2011, pp. 23–27 (2011)Google Scholar
  16. 16.
    Yang, J., Ran, P., Xiao, D., Tan, J.: Digital image forgery forensics by using undecimated dyadic wavelet transform and Zernike moments. J. Comput. Inf. Syst. 9(16), 6399–6408 (2013)Google Scholar
  17. 17.
    Li, L., Li, S., Zhu, H., Chu, S.-C., Roddick, J.F., Pan, J.-S.: An efficient scheme for detecting copy-move forged images by local binary patterns. J. Inf. Hiding Multimed. Signal Process. 4(1), 46–56 (2013)Google Scholar
  18. 18.
    Thajeel, S.A., Sulong, G.: A Novel Approach for Detection of Copy Move Forgery using Completed Robust Local Binary. J. Inf. Hiding Multimed. Signal Process. 6(2), 351–364 (2015)Google Scholar
  19. 19.
    Kim, H.S., Lee, H.-K.: Invariant image watermark using Zernike moments. IEEE Transactions on Circuits and Systems for Video Technology 13(8), 766–775 (2003)CrossRefGoogle Scholar
  20. 20.
    Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8(2), 179–187 (1962)CrossRefMATHGoogle Scholar
  21. 21.
    Ryu, S.J., Lee, M.J., Lee, H.K.: Detection of copy-rotate-move forgery using zernike moments. In: LNCS (LNAI, LNBI), vol. 6387, pp. 51–65 (2010)Google Scholar
  22. 22.
    Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proc. of the 30th Annual ACM Symposium on Theory of Computing, pp. 604–613 (1998)Google Scholar
  23. 23.
    Ke, Y., Sukthankar, R., Huston, L.: Efficient near-duplicate detection and sub-image retrieval categories and subject descriptors. In: Proc. of ACM International Conference on Multimedia (MM), pp. 869–876 (2004)Google Scholar
  24. 24.
    Cozzolino, D., Poggi, G., Verdoliva, L.: Copy-move forgery detection based on patchmatch. In: The International Conference on Image Processing (ICIP), pp. 5247–5251 (2014)Google Scholar
  25. 25.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  26. 26.
    Wagstaff, K., Cardie, C.: Constrained K-means clustering with background knowledge. In: The Eighteenth International Conference on Machine Learning, pp. 577–584 (2001)Google Scholar
  27. 27.
    Halder, A.: Color Image Segmentation using Rough Set based K-Means Algorithm. Int. J. Comput. Appl. 57(12), 32–38 (2012)Google Scholar
  28. 28.
    Sonagara, D., Badheka, S.: Comparison of Basic Clustering Algorithms. Int. J. Comput. Sci. Mob. Comput. 3(10), 58–61 (2014)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringUniversiti Sains MalaysiaGelugorMalaysia

Personalised recommendations