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

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Abstract:
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.

Keywords:
copy move, segmentation, SIFT, KLSP.

Citation:
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.

References:

  1. Luo W, Huang J, Qiu G. Robust detection of region duplication forgery in digital image. 18th International Conference on Pattern Recognition (ICPR'06) 2006; 4: 746-749.
  2. Lowe DG. Distinctive image features from scale-invariant key points. International Journal on Computer Vision 2004; 60(2): 91-110.
  3. Bay H, Ess A, Tuytelaars T, Van Gool L. SURF: Speeded up robust features. Computer Vision and Image Understanding 2008; 110(3): 346-359.
  4. Amerini I, Ballan L, Caldelli R, Del Bimbo A. A SIFT based forensic method for copy move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security 2011; 6(3): 1099-1110.
  5. Shen J, Du Y, Wang W, Li X. Lazy Random walks for Superpixel segmentation. IEEE Transactions on Image Processing 2014; 23(4): 1451-1462.
  6. Moore A, Prince S, Warrell J, Mohammed U, Jones G. Superpixel lattices. Proc IEEE CVPR 2008: 1-8.
  7. Levinshtein A, Stere A, Kutulakos K, Fleet J, Dickinson S, Siddiqi K. Turbopixels: Fast superpixels using geometric flows. IEEE Trans Pattern Anal Mach Intell 2009; 31(12): 2290-2297.
  8. Watkins DS. Fundamentals of Matrix Computations. 3rd ed. New York, NY: Wiley; 2010.
  9. Ren X, Malik J. Learning a classification model for segmentation. Proc 9th IEEE ICCV 2003: 10-17.
  10. Veksler O, Boykov Y, Mehrani P. Superpixels and supervoxels in an energy optimization framework. Proc ECCV 2010; V: 211-224.
  11. Achanta R, Shaji A, Smith K, Lucchi A, Fsua P, Sasstrunk S. SLIC superpixels. EPFL Tech Rep 149300. Lausanne, Switzerland: 2010.
  12. Xiang S, Pan C, Nie F, Zhang C. TurboPixel segmentation using eigen-images. IEEE Trans Image Process 2010; 19(11): 3024-3034.
  13. Yang X, Cai J, Zheng J, Luo J. User-friendly interactive image segmentation through unified combinatorial user inputs. IEEE Trans Image Process 2010; 19(9): 2470-2479.
  14. Rajalakshmi C, Dr Germanues Alex M, Dr Balasubramanian R. Study of image tampering and review of tampering detection techniques. International Journal of Advanced Research in Computer Science 2017; 8(7): 963-967.
  15. Lowe DG. Distinctive image features from scale invariant keypoints. International Journal of Computer Vision 2004; 60(2): 91-110.
  16. Christlein V, Riess Ch, Jordan J, Riess C, Angelopoulou E. An evaluation of popular copy move forgery detection approaches. IEEE Transaction on Information Forensics and Security 2012; 7(6): 1841-1854.
  17. Kaur H, Saxena J, Singh S. Simulative comparison of copy move forgery detection methods for digital images. International Journal of Electronics, Electrical and Computational System 2015; 4(special issue): 62-66.
  18. Xu B, Wang J, Liu G, Dai Y. Image copy move forgery detection based on SURF. International Conference on Multimedia Information Networking and Security (MINES) 2010.
  19. Hashmi MF, Hambared AR, Keskar AG. Copy move forgery detection using DWT and SIFT features. IEEE 13th International Conference on Intelligent System Design and Application (ISDA) 2013: 188-193.
  20. Li K, Li H, Yang B, Meng Q, Luo Sh. Detection of image forgery based on improved PCA-SIFT. Proceeding of International Conference on Computer Engineering and Network 2013; 679-686.
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