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Double Block-based Improved Copy-Move Forgery Detection

by Aspira S. Tripathy, Vikas Maheshkar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 10
Year of Publication: 2018
Authors: Aspira S. Tripathy, Vikas Maheshkar
10.5120/ijca2018917719

Aspira S. Tripathy, Vikas Maheshkar . Double Block-based Improved Copy-Move Forgery Detection. International Journal of Computer Applications. 182, 10 ( Aug 2018), 36-44. DOI=10.5120/ijca2018917719

@article{ 10.5120/ijca2018917719,
author = { Aspira S. Tripathy, Vikas Maheshkar },
title = { Double Block-based Improved Copy-Move Forgery Detection },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2018 },
volume = { 182 },
number = { 10 },
month = { Aug },
year = { 2018 },
issn = { 0975-8887 },
pages = { 36-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number10/29859-2018917719/ },
doi = { 10.5120/ijca2018917719 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:11:02.706352+05:30
%A Aspira S. Tripathy
%A Vikas Maheshkar
%T Double Block-based Improved Copy-Move Forgery Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 10
%P 36-44
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the advancement of handy and sophisticated image editing software, the alteration of digital images’ contents has become rife in all senses and camouflaged enough for non-identification. A digital image is a copious source of data and information that can be used for corroboration of factual events; however, in the recent times, its authenticity has developed a questionable background. Various types of forgery have come into picture within the past years. To counteract these forgeries, equal efforts have been focused on to make systems, inclusive of techniques to detect the forgeries. Keeping the accuracy, precision and the time complexity in mind, this paper focuses primarily on Discrete Cosine Transformation Block Based Copy-Move Forgery detection technique. The algorithm evaluation using blocks of sizes 8x8 and 16x16 are compared and contrasted to get a vivid idea about the advantages and shortcomings of using either of these. 8x8 blocks detect even the slightest tampering accurately, due to the small size of blocks, but yield a large number of false matches. Whereas, 16x16 blocks significantly reduce the number of false matches, but also the accuracy since now the minute forgeries aren’t detected. The information, thus received, has served as grounds in this paper to provide an improved algorithm, which combines the advantages of both the parts and then renders better results, comparatively.

References
  1. Kashyap et al. “An Evaluation Of Digital Image Forgery Detection Approcahes” in Press: https://arxiv.org/abs/1703.09968, 30th March 2017.
  2. J. Wang, G. Liu, et al, “Fast and robust Forensics for image region-duplication forgery,” Acta Automatica Sinica, Vol. 35, no. 12, pp. 1488-95, Dec. 2009.
  3. Alkawaz, M.H., Sulong, G., Saba, T. et al. Neural Comput. & Applic. (2018) 30: 183. https://doi.org/10.1007/s00521-016-2663-3
  4. Wang H., et al. (2016) An Efficient Passive Auth. Scheme for Copy-Move Forgery Based on DCT. In: Sun X., Liu A., Chao HC. Bertino E. (eds) Cloud Comp. & Security. ICCCS 2016. Lecture Notes in Computer Science, vol 10040. Springer, Cham.
  5. Mishra, A. et al.: A novel image-watermarking scheme using extreme learning machine. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–6 (2012).
  6. Tong, X. et al.: novel chaos-based fragile watermarking for image tampering detection and self-recovery signal process. Image Commun.28, 301–308 (2013).
  7. Fridrich, J., Soukal D., Lukas J.: Detection of copy-move forgery in digital images. In: Digital Forensic Research workshop, Cleveland, pp. 19–23 (2003).
  8. Zhou, L. et al: Blur detection of digital forgery using mathematical morphology. In: Nguyen, N.T., Grzech, A., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2007. LNCS (LNAI), vol. 4496, pp. 990–998. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72830-6 105
  9. Gaharwar et al: “Comprehensive Study of diff. types of Image Forgeries”, IJSTM, Vol. No. 04, Issue 01,August 2015 ISSN 2394-1537.
  10. Ashima Gupta et al.: “Detecting Copy-Move Forgery using DCT”, IJSRP, Volume 3, Issue 5, May 2013 1 ISSN 2250-3153.
  11. N. D. Wandji et al., “Detection of copy-move forgery in digital images based on DCT,” Journal of Computer Science, vol. 10, pp. 295–302, 2013.
  12. J. Zhao and J. Guo, “Passive forensics for copy-move image forgery using a method based on DCT and SVD,” Forensic Science International, vol. 233, no. 1–3, pp. 158–166, 2013.
  13. Popescu and H. Farid, “Exposing Digital Forgeries by Detecting Duplicated Image Regions,” Tech. Report, TR2004-515, Dartmouth College, Comp. Science, 2004.
  14. W. Luo, et al., Robust detection of region-duplication forgery in digital images, in: International Conference on Pattern Recognition Vol. 04, 2006, 746-749.
  15. Pameli Mukherjee et al, “A Review on Copy-Move Forgery Detection Techniques Based on DCT and DWT”, IJCSMC, Vol.4 Issue.3, March- 2015.
  16. Mukherjee S., Kumar S., and Desai J.,“A Fast DCT based Method for Copy-Move Forgery Detection”, in Proceedings of IEEE 2nd International Conference On Image Information Processing, pp 649-654, 2013.
  17. G. Li, et al., “A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries Based on DWT and SVD,” Proc. International Conference on Multimedia and Expo, pp. 1750-1753,2007.
  18. X. Kang, and S. Wei, “Identifying tampered regions using singular value decomposition in digital image forensics,” International Conference on Computer Science and Software Engineering, 2008, Vol. 3, pp. 926-930.
  19. Z. Lin et al., “Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis,” Pattern Recogn.,Vol. 42, pp. 2492-2250, 2009.
  20. Yao, Heng and Qiao, Tong and Tang, et al.: “Detecting Copy-Move Forgery Using Nonnegative Matrix Factorization”, Proceedings - 3rd International Conference on Multimedia Information Networking and Security, MINES 2011. 10.1109/MINES.2011.104.
  21. B. Xu, G. Liu and Y. Dai, “A Fast Image Copy-Move Forgery Detection Method Using Phase Correlation,” 4th International Conference on Multimedia Information Networking and Security, Nanjing, 2012, pp. 319-322.
  22. Yu, J., Han, Q.L.: Detection of copy-move forgery in digital images using discrete cosine transform and cluster analysis. In: International Conference on Computer Networks and Information Security, pp. 73–76 (2015).
  23. S. Bayram, et al., “An efficient and robust method for detecting copy-move forgery,” in Proceeding of the IEEE International Conf. on Acoustics, Speech, & Signal Processing (ICASSP’09), pp.1053–1056, Taipei, Taiwan.
  24. B.Mahdian and S. Saic, “Detection of copy-move forgery using a method based on blur moment invariants,” Forensic Science International, vol. 171, no. 2-3, pp. 180–189, 2007.
  25. Muthukumarasamy, Sridevi. (2012). Copy - Move Image Forgery Detection in a Parallel Environment. Computer Science & Information Technology. 2. 19-29. 10.5121/csit.2012.2303.
  26. Alahmadi, et al. (2016). Passive detection of image forgery using DCT and local binary pattern. Signal, Image and Video Proc. 11. 10.1007/s11760-016-0899-0.
  27. H. Huang, W. Guo, and Y. Zhang, “Detection of copy-move forgery in digital images using SIFT algorithm,” Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Vol. 2, pp. 272-6, Dec. 2008.
  28. X. Pan, S. Lyu, Region duplication detection using image feature matching, IEEE Trans. Inf. Forensics Secur. 5 (4) (2010) 857–867.
  29. Amerini et al., A SIFT-Based Forensic Method for copy-move attack detection and transformation recovery, IEEE Trans. Inf. Forensics Secur. 6 (3) (2011) 1099–1110.
  30. M. F. Hashmi et al., “Copy move forgery detection using DWT and SIFT features,” 13th International Conference on Intellient Systems Design and Applications, Bangi, 2013,pp.188-193.
Index Terms

Computer Science
Information Sciences

Keywords

DCT Copy move forgery detection Image processing Image Forensics Block based forgery detection image tampering passive forgery detection.

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