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Reseach Article

Automated Forensic Method for Copy-Move Forgery Detection based on Harris Interest Points and SIFT Descriptors

by B.L.Shivakumar, Dr. S.Santhosh Baboo
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 27 - Number 3
Year of Publication: 2011
Authors: B.L.Shivakumar, Dr. S.Santhosh Baboo
10.5120/3283-4472

B.L.Shivakumar, Dr. S.Santhosh Baboo . Automated Forensic Method for Copy-Move Forgery Detection based on Harris Interest Points and SIFT Descriptors. International Journal of Computer Applications. 27, 3 ( August 2011), 9-17. DOI=10.5120/3283-4472

@article{ 10.5120/3283-4472,
author = { B.L.Shivakumar, Dr. S.Santhosh Baboo },
title = { Automated Forensic Method for Copy-Move Forgery Detection based on Harris Interest Points and SIFT Descriptors },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 27 },
number = { 3 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 9-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume27/number3/3283-4472/ },
doi = { 10.5120/3283-4472 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:49.713438+05:30
%A B.L.Shivakumar
%A Dr. S.Santhosh Baboo
%T Automated Forensic Method for Copy-Move Forgery Detection based on Harris Interest Points and SIFT Descriptors
%J International Journal of Computer Applications
%@ 0975-8887
%V 27
%N 3
%P 9-17
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

We are undoubtedly living in an age where we are exposed to a remarkable array of visual imagery. Nowadays, accepting digital images of official documents is common practice. Image authenticity is important in many social areas. For instance, the trustworthiness of photographs has an essential role in courtrooms, where they are used as evidence. In the medical field, physicians make critical decisions based on digital images. The technology today makes it convenient to quickly exchange contracts, photographs or other documents. While we may have historically had confidence in the integrity of this imagery, today’s digital technology has begun to erode this trust. With the advent of low-cost and high-resolution digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered. It is possible to change the information represented by an image and create forgeries, which are indistinguishable by naked eye from authentic photographs and documents. In the proposed method Harris Interest Point detector along with SIFT descriptors are used to detect copy-move forgery. KD-Tree is used for matching.

References
  1. S. Lyu and H. Farid, “How realistic is photorealistic?” IEEE Transactions on Signal Processing, vol. 53, no. 2, pp. 845–850, 2005.
  2. I. J. Cox, M. L. Miller, and J. A. Bloom, Digital watermarking. San Francisco, CA: Morgan Kaufmann, 2002.
  3. B.L.Shivakumar and S.Santhosh Baboo, “Digital Image Forgery Detection”, SAJOSPS, Vol. 10(2), pp. 116-119, 2010
  4. H. Farid and S. Lyu, “Higher-order wavelet statistics and their applicationto digital forensics,” in Proc. of IEEE CVPR Workshop on StatisticalAnalysis in Computer Vision, Madison, WI, USA, 2003.
  5. S. Bayram, H.T. Sencar, and N. Memon, “A Survey of Copy-MoveForgery Detection Techniques,” in Proc. IEEEWestern New York ImageProcessing Workshop, Rochester, NY, USA, October 2008.
  6. B.L.Shivakumar and S.Santhosh Baboo, “Detecting Copy-Move Forgery in Digital Images:A Survey and Analysis of Current Methods”, GJCST, Vol 10(7), pp. 61-65, Sep. 2010
  7. J. Fridrich, D. Soukal, and J. Luk´as, “Detection of copy-move forgery in digital images,” in Proc. of DFRWS, 2003.
  8. G. Li, Q. Wu, D. Tu, and S. Sun, “A Sorted Neighborhood Approach for Detecting Duplicated Regions in Image Forgeries based on DWT and SVD,” in Proceedings of IEEE International Conference on Multimedia and Expo, Beijing China, July 2-5, 2007, pp. 1750-1753.
  9. A. C. Popescu and H. Farid, “Exposing digital forgeries by detectingtraces of resampling,” IEEE Transactions on Signal Processing, vol. 53,no. 2, pp. 758–767, 2005.
  10. S. Bayram, H. Taha Sencar, and N. Memon, “An efficient and robust method for detecting copy-move forgery,” in Proc. of IEEE ICASSP,Washington, DC, USA, 2009.
  11. E. S. Gopi, N. Lakshmanan, T. Gokul, S. KumaraGanesh, and P. R. Shah, “DigitalImage Forgery Detection using Artificial Neural Network and Auto Regressive Coefficients,” Electrical and Computer Engineering, 2006, pp.194-197.
  12. S.-J. Ryu, M.-J. Lee, and H.-K. Lee, “Detection of copy-rotate-move forgery using zernike moments,” in Proc. of UK, pp. 147-151, 1988.
  13. D. G. Lowe, “Object Recognition from Local Scale-Invariant Features,”in IEEE International Conference on Computer Vision (ICCV),Kerkyra, Greece, 1999, pp. 1150–1517.
  14. H. Bay, T. Tuytelaars, and L. V. Gool, “SURF: Speeded Up Robust Features,” in European Conference on Computer Vision (ECCV), Graz, Austria, 2006, pp. 404–417.
  15. J. Matas, O. Chum, M. Urban, and T. Pajdla, “Robust Wide Baseline Stereo from Maximally Stable Extremal Regions,” in British Machine Vision Conference (BMVC), vol. 1, London, UK, 2002, pp. 384–393.
  16. K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615–1630, 2005.
  17. Luo Juan, Oubong Gwun, A Comparison of SIFT, PCA-SIFT and SURF, IJIP, Vol. 3(4), 2009
  18. H. Huang, W. Guo, and Y. Zhang, “Detection of copy-move forgeryin digital images using SIFT algorithm,” in Proc. of IEEE Pacific-AsiaWorkshop on Computational Intell. and Industrial Application, 2008.
  19. X. Pan and S. Lyu, “Detecting image region duplication using SIFTfeatures,” in Proc. of IEEE ICASSP, Dallas, USA, 2010.
  20. I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, and G. Serra, “Geometric tampering estimation by means of a SIFT-based forensic analysis,” in Proc. of IEEE ICASSP, Dallas, USA, 2010.
  21. P. Azad, T. Asfour, and R. Dillmann, “Combining Harris Interest Points and the SIFT Descriptor for Fast Scale-Invariant Object Recognition”, In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), St. Louis, USA,2009.
  22. C. G. Harris and M. J. Stephens, “A Combined Corner and EdgeDetector,” in Alvey Vision Conference, Manchester, UK, pp.147–151,1988
  23. A.Moore. An introductory tutorial on KD-trees. Technical Report No. 209, University of Cambridge, 1991.
  24. V. Christlein, C. Riess, and E. Angelopoulou, “A Study on Features for the Detection of Copy-Move orgeries,” in GI SICHERHEIT, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Copy-Move Forgery Harris interest point SIFT KD-tree