Advertisement

Enhanced block-based copy-move forgery detection using k-means clustering

  • Osamah M. Al-Qershi
  • Bee Ee KhooEmail author
Article
  • 87 Downloads

Abstract

The goal of copy-move forgery is to manipulate the semantics of an image. In fact, this can be performed by cloning a region of an image and subsequently pasting it onto a different region within the same image. As such, this paper proposes an improved matching technique based on enhanced CMFD pipeline via k-means clustering technique. By deploying the k-means clustering to group the overlapping blocks, the matching step was independently carried out within each cluster to speed up the matching process. In addition, the clustering step of the feature vectors allowed the matching process to identify the matches accurately. Thus, in order to test the enhanced pipeline, it was combined with Zernike moments and locality sensitive hashing (LSH). The experimental results proved that the proposed method can enhance the detection accuracy in a significant manner. On top of that, the proposed pipeline can reduce the processing time with LSH-based matching.

Keywords

Image forgery detection Copy-move forgery Clustering 

Notes

Acknowledgements

The authors would like to acknowledge the financial assistance provided by the Malaysian Ministry of Education through FRGS Grant No. 203/PELECT/6071305.

References

  1. Al-Qershi, O. M., & Khoo, B. E. (2013). Passive detection of copy-move forgery in digital images: state-of-the-art. Forensic Science International, 231(1–3), 284–295.CrossRefGoogle Scholar
  2. Al-Qershi, O. M., Khoo, B. E. (2014). Enhanced matching method for copy-move forgery detection by means of Zernike moments. In Lecture notes in computer science, LNCS (Vol. 9023, pp. 485–497). Berlin: Springer.CrossRefGoogle Scholar
  3. Al-Qershi, O. M., & Khoo, B. E. (2016). Copy-move forgery detection using locality sensitive hashing and k-means clustering. In Proceedings of the information science and applications (ICISA) (pp. 663–672).Google Scholar
  4. Amerini, I., Barni, M., Caldelli, R., & Costanzo, A. (2013). Removal and injection of keypoints for SIFT-based copy-move counter-forensics. EURASIP Journal on Information Security, 8, 1–12.Google Scholar
  5. Bakiah, N., et al. (2016). Copy-move forgery detection: Survey, challenges and future directions. Journal of Network and Computer Applications, 75, 259–278.CrossRefGoogle Scholar
  6. Bo, X., Junwen, W., Guangjie, L., & Yuewei, D. (2010). Image copy-move forgery detection based on SURF. In International conference on multimedia information networking and security (MINES) (pp. 889–892).Google Scholar
  7. Caldelli, R., Amerini, I., & Costanzo, A. (2015).. Sift match removal and keypoint preservation through dominant orientation shift. In Proceedings of the 23rd European signal processing conference (EUSIPCO) (pp. 2107–2111).Google Scholar
  8. Christlein, V., Riess, C., & Angelopoulou, E. (2010a). On rotation invariance in copy-move forgery detection. In IEEE international workshop on information forensics and security, WIFS.Google Scholar
  9. Christlein, V., Riess, C., & Angelopoulou, E. (2010b). A study on features for the detection of copy-move forgeries. In Proceedings of the information security solutions Europe (pp. 105–116).Google Scholar
  10. Christlein, V., Riess, C., Jordan, J., & Angelopoulou, E. (2012). An evaluation of popular copy-move forgery detection approaches. IEEE Transactions on Information Forensics and Security, 7(6), 1841–1854.CrossRefGoogle Scholar
  11. Emam, M., Han, Q., & Niu, X. (2015). PCET based copy-move forgery detection in images under geometric transforms. Multimedia Tools and Applications, 75(18), 11513–11527.CrossRefGoogle Scholar
  12. Halder, A. (2012). Color image segmentation using rough set based K-means algorithm. International Journal of Computers and Applications, 57(12), 32–38.Google Scholar
  13. Jinke, X. (2016). Image forgery detection algorithm based on non sampling wavelet transform and Zernike moments. International Journal of Security and its Applications, 10(2), 27–38.CrossRefGoogle Scholar
  14. Karsh, R. K., Das, A., Swetha, G. L., Medhi, A. & Laskar, R. H. (2016). Copy-move forgery detection using ASIFT. In 1st India international conference on information processing (IICIP) (pp. 1–5).Google Scholar
  15. Ketenci, S., & Ulutas, G. (2013). Copy-move forgery detection in images via 2D-Fourier Transform. In Proceedings of the 36th international conference on telecommunications and signal processing (TSP) (pp. 813–816).Google Scholar
  16. Lai, Y., Huang, T., Lin, J., & Lu, H. (2017). An improved block-based matching algorithm of copy-move forgery detection. Multimedia Tools and Applications, 77, 15093.CrossRefGoogle Scholar
  17. Langille, A., & Gong, M. (2006). An efficient match-based duplication detection algorithm. In Third Canadian conference on computer and robot vision, CRV 2006 (Vol. 2006, pp. 64–71).Google Scholar
  18. Lee, J., Chang, K., Chang, C., & Cheng, C. (2014). Image copy-move forgery detection based on HOG. In Proceedings of the 27th IPPR conference on computer vision, graphics, and image processing (pp. 1–5).Google Scholar
  19. Li, Y. (2013). Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Science International, 224(1–3), 59–67.CrossRefGoogle Scholar
  20. Lin, C.-S., Chen, C.-C., & Chang, Y.-C. (2015). An efficiency enhanced cluster expanding block algorithm for copy-move forgery detection. In Proceedings of the international conference on intelligent networking and collaborative systems (pp. 228–231).Google Scholar
  21. Liu, F., & Feng, H. (2014). An efficient algorithm for image copy-move forgery detection based on DWT and SVD. International Journal of Security and Its Applications, 8(5), 377–390.CrossRefGoogle Scholar
  22. Liu, Y., Guan, Q., & Zhao, X. (2017). Copy-move forgery detection based on convolutional kernel network. Multimedia Tools and Applications, 77, 1–25.Google Scholar
  23. Lynch, G., Shih, F. Y., & Liao, H.-Y. M. (2013). An efficient expanding block algorithm for image copy-move forgery detection. Information Sciences (New York), 239, 253–265.CrossRefGoogle Scholar
  24. Mahdian, B., & Saic, S. (2010). A bibliography on blind methods for identifying image forgery. Signal Processing: Image Communication, 25(6), 389–399.Google Scholar
  25. Mohebbian, E., & Hariri, M. (2015). Increase the efficiency of DCT method for detection of copy-move forgery in complex and smooth images. In Proceedings of the 2nd international conference on knowledge-based engineering and innovation, KBEI (pp. 436–440).Google Scholar
  26. Pan, X., & Lyu, S. (2010). Detecting image region duplication using SIFT features. In 2010 IEEE international conference on acoustics speech and signal processing (ICASSP) (pp. 1706–1709).Google Scholar
  27. Park, C.-S., Kim, C., Lee, J., & Kwon, G.-R. (2016). Rotation and scale invariant upsampled log-polar fourier descriptor for copy-move forgery detection. Multimedia Tools and Applications, 75, 1–19.CrossRefGoogle Scholar
  28. Redi, J. A., Taktak, W., & Dugelay, J. L. (2011). Digital image forensics: A booklet for beginners. Multimedia Tools and Applications, 51(1), 133–162.CrossRefGoogle Scholar
  29. Ryu, S.-J., Kirchner, M., Lee, M.-J., & Lee, H.-K. (2013). Rotation invariant localization of duplicated image regions based on zernike moments. IEEE Transactions on Information Forensics and Security, 8(8), 1355–1370.CrossRefGoogle Scholar
  30. Shivakumar, B. L., & Baboo, S. (2011). Detection of region duplication forgery in digital images using SURF. International Journal of Computer Science Issues, 8(4), 199–205.Google Scholar
  31. Silva, E., Carvalho, T., Ferreira, A., & Rocha, A. (2015). Going deeper into copy-move forgery detection: Exploring image telltales via multi-scale analysis and voting processes. Journal of Visual Communication and Image Representation, 29, 16–32.CrossRefGoogle Scholar
  32. Singh, J., & Raman, B. (2012). A high performance copy-move image forgery detection scheme on GPU. Advances in Intelligent and Soft Computing, vol. 131, 2, 239–246.CrossRefGoogle Scholar
  33. Sonagara, D., & Badheka, S. (2014). Comparison of Basic Clustering Algorithms. International Journal of Computer Science and Mobile Computing, 3(10), 58–61.Google Scholar
  34. Thajeel, S. A., & Sulong, G. (2015). A novel approach for detection of copy move forgery using completed robust local binary pattern. Journal of Information Hiding and Multimedia Signal Processing, 6(2), 351–364.Google Scholar
  35. Tralic, D., Zupancic, I., Grgic, S., & Grgic M. (2013). CoMoFoD: New database for copy-move forgery detection. In Proceedings of 55th international symposium ELMAR (pp. 49–54).Google Scholar
  36. Uliyan, D., Jalab, H., Abdul Wahab, A., & Sadeghi, S. (2016). Image region duplication forgery detection based on angular radial partitioning and Harris key-points. Symmetry (Basel), 8(7), 62.MathSciNetCrossRefGoogle Scholar
  37. Ustubioglu, B., Ulutas, G., Ulutas, M., & Nabiyev, V. (2016). A new copy move forgery detection technique with automatic threshold determination. AEU-International Journal of Electronics and Communications, 70(8), 1076–1087.CrossRefGoogle Scholar
  38. Wagstaff, K., & Cardie, C. (2001). Constrained K-means clustering with background knowledge. In The eighteenth international conference on machine learning (pp. 577–584).Google Scholar
  39. Yadav, P., & Rathore, Y. (2012). Detection of copy-move forgery of images using discrete wavelet transform. International Journal of Computational Science and Engineering, 4, 565–570.Google Scholar
  40. Zhang, Z., Wang, D., Wang, C., & Zhou, X. (2017). Detecting copy-move forgeries in images based on DCT and main transfer vectors. KSII Transactions on Internet and Information Systems, 11(9), 4567–4587.Google Scholar
  41. Zhao, P., Li, S., Zhou, L., Li, L., & Guo, X. (2015). Detecting affine-distorted duplicated regions in images by color histograms. Journal of Information Hiding and Multimedia Signal Processing, 6(1), 163–174.Google Scholar
  42. Zheng, N., Wang, Y., & Xu, M. (2013). A LBP-based method for detecting copy-move forgery with rotation. Lecture Notes in Electrical Engineering, 240, 261–267.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringUniversiti Sains Malaysia (USM)PenangMalaysia

Personalised recommendations