Copy move forgery detection using key point localized super pixel based on texture features
Rajalakshmi C., Germanus Al.M., Balasubramanian R.

Research scholar Roll No:12332, Dept. of Computer Science, Manonmaniam Sundaranar University, Abishekapatti,Tirunelveli 627012,Tamil Nadu, India,
Dept. of Computer Science, Kamarajar Government Arts College, Surandai,

Dept. of Computer Science & Engg., Manonmaniam Sundaranar University, Abishekapatti,Tirunelveli

Аннотация:
The most important barrier in the image forensic is to ensue a forgery detection method such can detect the copied region which sustains rotation, scaling reflection, compressing or all. Traditional SIFT method is not good enough to yield good result. Matching accuracy is not good. In order to improve the accuracy in copy move forgery detection, this paper suggests a forgery detection method especially for copy move attack using Key Point Localized Super Pixel (KLSP). The proposed approach harmonizes both Super Pixel Segmentation using Lazy Random Walk (LRW) and Scale Invariant Feature Transform (SIFT) based key point extraction. The experimental result indicates the proposed KLSP approach achieves better performance than the previous well known approaches.

Ключевые слова:
copy move, segmentation, SIFT, KLSP.

Цитирование:
Rajalakshmi C, Alex MG, Balasubramanian R. Copy move forgery detection using key point localized super pixel based on texture features. Computer Optics 2019; 43(2): 270-276. DOI: 10.18287/2412-6179-2019-43-2-270-276.

Литература:

  1. Luo, W. Robust detection of region duplication forgery in digital image / W. Luo, J. Huang, G. Qiu // 18th International Conference on Pattern Recognition (ICPR'06). – 2006. – Vol. 4. – P. 746-749.
  2. Lowe, D.G. Distinctive image features from scale-invariant key points / D.G. Lowe // International Journal on Computer Vision. – 2004. – Vol. 60, Issue 2. – P. 91-110.
  3. Bay, H. SURF: Speeded up robust features / H. Bay, A. Ess, T. Tuytelaars, L. Van Gool // Computer Vision and Image Understanding. – 2008. – Vol. 110, Issue 3. – P. 346-359.
  4. Amerini, I. A SIFT based forensic method for copy move attack detection and transformation recovery / I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo // IEEE Transactions on Information Forensics and Security. – 2011. – Vol. 6, Issue 3. – P. 1099-1110.
  5. Shen, J. Lazy Random walks for Superpixel segmentation / J. Shen, Y. Du, W. Wang, X. Li // IEEE Transactions on Image Processing. – 2014. – Vol. 23, Issue 4. – P. 1451-1462.
  6. Moore, A. Superpixel lattices / A. Moore, S. Prince, J. Warrell, U. Mohammed, G. Jones // 2008 IEEE Conference on Computer Vision and Pattern Recognition. – 2008. – P. 1-8.
  7. Levinshtein, A. Turbopixels: Fast superpixels using geometric flows / A. Levinshtein, A. Stere, K. Kutulakos, J. Fleet, S. Dickinson, K. Siddiqi // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 2009. – Vol. 31, Issue 12. – P. 2290-2297.
  8. Watkins, D.S. Fundamentals of Matrix Computations / D.S. Watkins. – 3rd ed. – New York, NY: Wiley, 2010.
  9. Ren, X. Learning a classification model for segmentation / X. Ren, J. Malik // Proceedings 9th IEEE International Conference on Computer Vision. – 2003. – P. 10-17.
  10. Veksler, O. Superpixels and supervoxels in an energy optimization framework / O. Veksler, Y. Boykov, P. Mehrani // Proceedings of the 11th European conference on Computer vision. – 2010. – Part V. – P. 211-224.
  11. Achanta, R. SLIC superpixels / R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fsua, S. Sasstrunk. – EPFL Technical Report no. 149300. – Lausanne, Switzerland: 2010.
  12. Xiang, S. TurboPixel segmentation using eigen-images / S. Xiang, C. Pan, F. Nie, C. Zhang // IEEE Transactions on Image Processing. – 2010. – Vol. 19, Issue 11. – P. 3024-3034.
  13. Yang, X. User-friendly interactive image segmentation through unified combinatorial user inputs / X. Yang, J. Cai, J. Zheng, J. Luo // IEEE Transactions on Image Processing. – 2010. – Vol. 19, Issue 9. – P. 2470-2479.
  14. Rajalakshmi, C. Study of image tampering and review of tampering detection techniques / C. Rajalakshmi, Dr. M. Germanues Alex, Dr. R. Balasubramanian // International Journal of Advanced Research in Computer Science. – 2017. – Vol. 8, No. 7. – P. 963-967.
  15. Lowe, D.G. Distinctive image features from scale invariant keypoints / D.G. Lowe // International Journal of Computer Vision. – 2004. – Vol. 60, Issue 2. – P. 91-110.
  16. Christlein, V. An evaluation of popular copy move forgery detection approaches / V. Christlein, Ch. Riess, J. Jordan, C. Riess, E. Angelopoulou // IEEE Transaction on information Forensics and Security. – 2012. – Vol. 7, Issue 6. – P. 1841-1854.
  17. Kaur, H. Simulative comparison of copy move forgery detection methods for digital images / H. Kaur, J. Saxena, S. Singh // International Journal of Electronics, Electrical and Computational System. – 2015. – Vol. 4, special issue. – P. 62-66.
  18. Xu, B. Image copy move forgery detection based on SURF / B. Xu, J. Wang, G. Liu, Y. Dai // International Conference on Multimedia Information Networking and security (MINES). – 2010.
  19. Hashmi, M.F. Copy move forgery detection using DWT and SIFT features / M.F. Hashmi, A.R. Hambared, A.G. Keskar // IEEE 13th International Conference on Intelligent System Design and Application (ISDA). – 2013. – P. 188-193.
  20. Li, K. Detection of Image Forgery Based on Improved PCA-SIFT / K. Li, H. Li, B. Yang, Q. Meng, Sh. Luo // Proceeding of International Conference on Computer Engineering and Network. – 2013. – P. 679-686.

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