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Reseach Article

A Scale Invariant Digital Image Copy-Paste Forgery Detection Approach Based on NCC

Published on August 2016 by Anil Dada Warbhe, Rajiv V. Dharaskar, Vilas M. Thakare
International Conference on Communication Computing and Virtualization
Foundation of Computer Science USA
ICCCV2016 - Number 2
August 2016
Authors: Anil Dada Warbhe, Rajiv V. Dharaskar, Vilas M. Thakare
ac6589e1-c939-42ad-aeaa-d92326a35216

Anil Dada Warbhe, Rajiv V. Dharaskar, Vilas M. Thakare . A Scale Invariant Digital Image Copy-Paste Forgery Detection Approach Based on NCC. International Conference on Communication Computing and Virtualization. ICCCV2016, 2 (August 2016), 16-23.

@article{
author = { Anil Dada Warbhe, Rajiv V. Dharaskar, Vilas M. Thakare },
title = { A Scale Invariant Digital Image Copy-Paste Forgery Detection Approach Based on NCC },
journal = { International Conference on Communication Computing and Virtualization },
issue_date = { August 2016 },
volume = { ICCCV2016 },
number = { 2 },
month = { August },
year = { 2016 },
issn = 0975-8887,
pages = { 16-23 },
numpages = 8,
url = { /proceedings/icccv2016/number2/25603-0178/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Communication Computing and Virtualization
%A Anil Dada Warbhe
%A Rajiv V. Dharaskar
%A Vilas M. Thakare
%T A Scale Invariant Digital Image Copy-Paste Forgery Detection Approach Based on NCC
%J International Conference on Communication Computing and Virtualization
%@ 0975-8887
%V ICCCV2016
%N 2
%P 16-23
%D 2016
%I International Journal of Computer Applications
Abstract

It is very important to authenticate the digital image for its authenticity. The issue of the authenticity and integrity of digital images is progressively critical. Day by day, it's getting easier to create image forgeries because of the sophisticated image editing software programs. There are various types of digital image manipulation or tampering possible; like image compositing, splicing, copy-paste, etc. And Digital Image Forensics plays a vital role in proving its authenticity and integrity in such cases. The Copy-Paste forgery, also known as Copy-Move Forgery is a most common and popular type of digital image forgery. In this type of forgery, a region from an image copied and pasted afterward to an another location of the same picture. The main intention of the forger to do this is to conceal a vital portion in the scene. In this paper, a passive, scaling robust algorithm for the detection of Copy-Paste forgery is proposed. Sometimes the copied region of an image is scaled before pasting to some other location in the image. In such cases, the normal Copy-Paste detection algorithm fails to detect the forgeries. The approach is based on NCC (Normalized Cross Correlation) For this an improved customized Normalized Cross Correlation has been implemented and used for detecting highly correlated areas from the image and the image blocks, thereby detecting the tampered regions from an image. The experimental results demonstrate that the proposed approach can be effectively used to detect copy-paste forgeries accurately and is scaling robust. The proposed algorithm is also computationally efficient.

References
  1. The Internet Organised Crime Threat Assessment (iOCTA) [Internet]. Hague: Europol's European Cybercrime Centre (EC3); 2015 Oct. Available from: https://www. europol. europa. eu/sites/default/files/publications/europol_iocta_web_2015. pdf
  2. Kizza JM. Computer and Network Forensics. In: Guide to Computer Network Security. Springer; 2015. p. 299–324.
  3. Danube Adria Association for Automation & Manufacturing, Katalini? B, Buchmeister B, Geršak J, DAAAM International (Vienna), editors. DAAAM International scientific book 2014. Vienna: DAAAM International Vienna; 2014.
  4. ?isar P, ?ISAR SM. General Directions of Development In Digital Forensics. Acta Tech Corvininesis-Bull Eng. 2012;5(2).
  5. Karie NM, Venter HS. Toward a General Ontology for Digital Forensic Disciplines. J Forensic Sci. 2014;59(5):1231–41.
  6. Dehnie S. Digital image forensics for identifying computer generated and digital camera images. In: Image Processing, 2006 IEEE International Conference on. IEEE; 2006. p. 2313–6.
  7. Warbhe A, Dharaskar R, Thakare V. Block Based Image Forgery Detection Techniques. Int J Eng Sci Res Technol. 2015;4(8):289–97.
  8. Farid H. Image forgery detection. Signal Process Mag IEEE. 2009;26(2):16–25.
  9. Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E. An evaluation of popular copy-move forgery detection approaches. Inf Forensics Secur IEEE Trans On. 2012;7(6):1841–54.
  10. Qureshi MA, Deriche M. A bibliography of pixel-based blind image forgery detection techniques. Signal Process Image Commun. 2015;39:46–74.
  11. Fridrich AJ, Soukal BD, Lukáš AJ. Detection of copy-move forgery in digital images. In: in Proceedings of Digital Forensic Research Workshop. Citeseer; 2003.
  12. Farid A, Popescu A. Exposing digital forgeries by detecting duplicated image regions. Technical Report, TR2004-515, Department of Computer Science, Dartmouth College, Hanover, New Hampshire; 2004.
  13. Myrna A, Venkateshmurthy M, Patil C. Detection of region duplication forgery in digital images using wavelets and log-polar mapping. In: Conference on Computational Intelligence and Multimedia Applications, 2007 International Conference on. IEEE; 2007. p. 371–7.
  14. Bayram S, Sencar HT, Memon N. An efficient and robust method for detecting copy-move forgery. In: Acoustics, Speech and Signal Processing, 2009 ICASSP 2009 IEEE International Conference on. IEEE; 2009. p. 1053–6.
  15. Li W, Yu N. Rotation robust detection of copy-move forgery. In: Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEE; 2010. p. 2113–6.
  16. Kang L, Cheng X. Copy-move forgery detection in digital image. In: Image and Signal Processing (CISP), 2010 3rd International Congress on. 2010. p. 2419–21.
  17. Langille A, Gong M. An efficient match-based duplication detection algorithm. In: Computer and Robot Vision, 2006 The 3rd Canadian Conference on. IEEE; 2006. p. 64–64.
  18. Christlein V, Riess C, Angelopoulou E. A Study on Features for the Detection of Copy-Move Forgeries. In: Sicherheit. 2010. p. 105–16.
  19. Mahdian B, Saic S. Detection of copy–move forgery using a method based on blur moment invariants. Forensic Sci Int. 2007;171(2):180–9.
  20. Lin H-J, Wang C-W, Kao Y-T, others. Fast copy-move forgery detection. WSEAS Trans Signal Process. 2009;5(5):188–97.
  21. Imamoglu MB, Ulutas G, Ulutas M. Detection of copy-move forgery using krawtchouk moment. In: Electrical and Electronics Engineering (ELECO), 2013 8th International Conference on. IEEE; 2013. p. 311–4.
  22. Quan X, Zhang H. Copy-move forgery detection in digital images based on local dimension estimation. In: Cyber Security, Cyber Warfare and Digital Forensic (CyberSec), 2012 International Conference on. IEEE; 2012. p. 180–5.
  23. Kumar S, Desai J, Mukherjee S. A fast DCT based method for copy move forgery detection. In: Image Information Processing (ICIIP), 2013 IEEE Second International Conference on. IEEE; 2013. p. 649–54.
  24. Yang B, Sun X, Chen X, Zhang J, Li X. An Efficient Forensic Method for Copy–move Forgery Detection based on DWT-FWHT. Radioengineering. 2013;22(4).
  25. Zhang J, Feng Z, Su Y. A new approach for detecting copy-move forgery in digital images. In: Communication Systems, 2008 ICCS 2008 11th IEEE Singapore International Conference on. IEEE; 2008. p. 362–6.
  26. 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: Multimedia and Expo, 2007 IEEE International Conference on. IEEE; 2007. p. 1750–3.
  27. Muhammad N, Hussain M, Muhammad G, Bebis G. Copy-move forgery detection using dyadic wavelet transform. In: Computer Graphics, Imaging and Visualization (CGIV), 2011 Eighth International Conference on. IEEE; 2011. p. 103–8.
  28. Singh VK, Tripathi R. Fast and efficient region duplication detection in digital images using sub-blocking method. Int J Adv Sci Technol. 2011;35:93–102.
  29. Mohamadian Z, Pouyan AA. Detection of Duplication Forgery in Digital Images in Uniform and Non-uniform Regions. In: Computer Modelling and Simulation (UKSim), 2013 UKSim 15th International Conference on. IEEE; 2013. p. 455–60.
  30. Cozzolino D, Poggi G, Verdoliva L. Copy-move forgery detection based on patchmatch. In: Image Processing (ICIP), 2014 IEEE International Conference on. IEEE; 2014. p. 5312–6.
  31. Barnes C, Shechtman E, Goldman DB, Finkelstein A. The generalized patchmatch correspondence algorithm. In: Computer Vision–ECCV 2010. Springer; 2010. p. 29–43.
  32. Sekeh MA, Maarof MA, Rohani MF, Mahdian B. Efficient image duplicated region detection model using sequential block clustering. Digit Investig. 2013;10(1):73–84.
  33. Zandi M, Mahmoudi-Aznaveh A, Mansouri A. Adaptive matching for copy-move Forgery detection. In: Parallel Computing Technologies (PARCOMPTECH), 2015 National Conference on. IEEE; 2015. p. 119–24.
  34. Cozzolino D, Gragnaniello D, Verdoliva L. Image forgery detection through residual-based local descriptors and block-matching. In: Image Processing (ICIP), 2014 IEEE International Conference on. IEEE; 2014. p. 5297–301.
  35. Cao G, Zhao Y, Ni R, Li X. Contrast enhancement-based forensics in digital images. Inf Forensics Secur IEEE Trans On. 2014;9(3):515–25.
  36. Liang Z, Yang G, Ding X, Li L. An efficient forgery detection algorithm for object removal by exemplar-based image inpainting. J Vis Commun Image Represent. 2015;30:75–85.
  37. Lee J-C, Chang C-P, Chen W-K. Detection of copy–move image forgery using histogram of orientated gradients. Inf Sci. 2015;
  38. Shin Y-D. Fast Detection of Copy-Move Forgery Image using DCT. J Korea Multimed Soc. 2013;16(4):411–7.
  39. Shin Y-D. Fast detection of copy-move forgery image using three step search algorithm in the spatial domain. In: Convergence and Hybrid Information Technology. Springer; 2012. p. 389–95.
  40. Shin Y-D. Fast detection of Duplicated Forgery Image using Sub-sampling. J Converg Inf Technol. 2015;10(2):17.
  41. Lewis J. Fast normalized cross-correlation. In: Vision interface. 1995. p. 120–3.
  42. Zitova B, Flusser J. Image registration methods: a survey. Image Vis Comput. 2003;21(11):977–1000.
  43. Brunelli R. Template matching techniques in computer vision. Wiley; 2008.
  44. Tralic D, Zupancic I, Grgic S, Grgic M. CoMoFoD—New database for copy-move forgery detection. In: ELMAR, 2013 55th International Symposium. IEEE; 2013. p. 49–54.
Index Terms

Computer Science
Information Sciences

Keywords

Image Forensics Digital Image Forensics Image Forgery Detection Image Tampering Image Authentication