CFP last date
20 June 2024
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 20 June 2024

Submit your paper
Know more
Reseach Article

Non-Overlapping Block-based Parametric Forgery Detection Model

by Kusam Sharma, Pawanesh Abrol
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 3
Year of Publication: 2016
Authors: Kusam Sharma, Pawanesh Abrol

Kusam Sharma, Pawanesh Abrol . Non-Overlapping Block-based Parametric Forgery Detection Model. International Journal of Computer Applications. 133, 3 ( January 2016), 17-24. DOI=10.5120/ijca2016907773

@article{ 10.5120/ijca2016907773,
author = { Kusam Sharma, Pawanesh Abrol },
title = { Non-Overlapping Block-based Parametric Forgery Detection Model },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 3 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 17-24 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016907773 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:30:06.324692+05:30
%A Kusam Sharma
%A Pawanesh Abrol
%T Non-Overlapping Block-based Parametric Forgery Detection Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 3
%P 17-24
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Modification of a digital image by adding or removing some of its elements using a wide variety of image processing tools results in image forgery. As a result authentication of originality of a digital image is becoming a challenging task. Copy-paste forgery is one of the forgeries belonging to context based forgery. Copy-Paste Forgery Detection (CPFD) aims at finding regions that have been copied and pasted within the same or different image. A small change in the image may change statistical parameters that can be analysed for initial assessment of the forgery. In the present research study, a parametric forgery detection model using non-overlapping block-based technique is developed to ascertain the copy-paste forgery in a given digital image. Statistical parameters of the input image are computed, analysed and compared with those of the forged image. The results show that the proposed model identifies the forged area of the given image and works well with low to moderate copy-paste forgery. The results obtained can be used as the initial verification of the images for forgery and to enhance the forgery detection process by identifying most likely cases of possible image forgeries. The proposed model is tested with large domain of images having different dimensions and for detecting forgery within an image. However, the model has limitations with certain geometrical transformations.

  1. Kusam, P. Abrol and Devanad, “Digital Tampering Detection Techniques: A Review”, BVICAM’s International Journal of Information Technology, vol. 1, no. 2, pp. 125-132, 2009.
  2. A. Sharma and P. Abrol, “Eye Gaze Techniques for Human Computer Interaction: A Research Survey”, International Journal of Computer Applications, vol. 71, no. 9, pp. 18-29, May 2013.
  3. V. Kumar and P. Gupta, “Importance of statistical measures in digital image processing”, International Journal of Emerging Technology and Advanced Engineering, vol. 2, Aug. 2012.
  4. Tajrobekar, M. 2014. Where must we use variance and mean of image? Available online at:. Where_must_we_use_variance_and_mean_of_image.
  5. Wegmuller, M., Weid, J.P., Oberson, P. and Gisin, N. 2000. High resolution fiber distributed measurements with coherent OFDR. In Proc. ECOC’00, paper 11.3.4, p. 109.
  6. Statistics: Standard deviation Available online at:. variance_std_deviation/v/statistics-standard-deviation.
  7. Wheeler, D.J. 2011. Problems with Skewness and Kurtosis, Part One. Available online at:. quality-insider-article/problems-skewness-and-kurtosis-part-one.html.
  8. Wheeler, D.J. 2011. Problems with Skewness and Kurtosis, Part Two. Available online at:. quality-insider-article/problems-skewness-and-kurtosis-part-two.html.
  9. Wu, Q., Wang, S. and Zhang X. 2010. Detection of image region-duplication with rotation and scaling tolerance. Computational Collective Intelligence, Technologies and Applications - Second International Conference, ICCCI, Part I, vol. 6421, pp. 100-108.
  10. Pan, X. and Lyu, S. 2010. Region duplication detection using image feature matching. IEEE Transactions of Information Forensics and Security, vol. 5, no. 4, pp. 857-867.
  11. B. L. Shivakumar and S. S. Baboo, “Detection of region duplication forgery in digital images using SURF”, International Journal of Computer Science Issues, vol. 8, no. 1, pp. 199-205, Jul. 2011.
  12. Kang, L. and Cheng, X. 2010. Copy-move forgery detection in digital image. IEEE 3rd International Congress on Image and Signal Processing, vol.5, pp. 2419-2421.
  13. Li, G., Wu, Q., Tu, D. and Sun, S. 2007. A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. IEEE International Conference on Multimedia and Expo, pp. 1750-1753.
  14. D. Sharma and P. Abrol, “Investigating the Extent of Noise in Digital Images using SVD”, International Journal of Software and Web Sciences, vol. 4, no. 1, pp. 6-14, May 2013.
  15. M. Zimba and S. Xingming, “DWT-PCA (EVD) based copy-move image forgery detection”, International Journal of Digital Content Technology and its Applications, vol. 5, no. 1, pp. 251-258, Jan. 2011.
  16. Muhammad, N., Hussain, M., Muhamad, G. and Bebis, G. 2011. A Non-Intrusive Method for Copy-Move Forgery Detection. ISVC, Part II, LNCS, Springer-Verlag, vol. 6939, pp. 516-525.
  17. Bayram, S., Sencar, H. T. and Memon, N. 2009. An efficient and robust method for detecting copy-move forgery. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1053-1056.
  18. Lin, H. J., Wang, C.W. and Kao, Y.T. 2009. Fast copy–move forgery detection. WSEAS Transactions on Signal Processing, vol. 5, no. 5, pp. 188–197.
  19. Wang, X., Zhang, X., Li, Z. and Wang, S. 2011. A DWT-DCT based passive forensics method for copy-move attacks. IEEE Third International Conference on Multimedia Information Networking and Security, Nov. 2011, pp. 304-308.
  20. Y. Huang, W. Lu and D. Long, “Improved DCT-based detection of copy-move forgery in images”, Elsevier, Forensic Science International, vol. 206, issues 1-3, pp. 178-184, March 2011.
  21. Pan, X., Zhang, X. and Lyu, S. 2011. Exposing image forgery with blind noise estimation. Proceedings of the 13th ACM multimedia workshop on Multimedia and Security, pp. 15-20.
  22. B. Mahdian and S. Saic, “Using noise inconsistencies for blind image forensics”, Elsevier, Image and Vision Computing, vol. 27, issue 10, pp. 1497-1503, Sept. 2009.
  23. Fridrich, J., Soukal, D. and Lukáš, J. 2003. Detection of copy-move forgery in digital images. In Proceedings of Digital Forensic Research Workshop, Cleveland OH, USA.
  24. J. Zhao and J. Guo, “Passive forensics for copy-move image forgery using a method based on DCT and SVD”, Elsevier, Forensic Science International, vol. 233, issues 1-3, pp. 158-166, Dec. 2013.
  25. Luo, W., Huang, J. and Qiu, G. 2006. Robust Detection of Region Duplication Forgery in Digital Images. In Proceedings of the 18th International Conference on Pattern Recognition, vol. 4, pp. 746-749.
  26. Ardizzone, E., Bruno, A. and Mazzola, G. 2010. Copy-move forgery detection via Texture Description. Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence, pp. 59-64.
  27. S. Khan and A. Kulkarni, “An efficient method for detection of copy-move forgery using Discrete Wavelet Transform”, International Journal on Computer Science and Engineering, vol. 2, no. 5, pp. 1801-1806, 2010.
  28. A.M. Sundaram and C. Nandini, “Feature based image authentication using symmetric surround saliency mapping in image forensics”, International Journal of Computer Applications, vol. 104, no. 13, pp. 43-51, October 2014.
  29. Popescu, A. C. and Farid, H. 2004. Exposing digital forgeries by detecting duplicated image regions. Technical Report. Department of Computer Science, Dartmouth College.
  30. Dattatherya, S.V. Chalam and M.K. Singh, “A generalized image authentication based on statistical moments of color histogram”, ACEEE International Journal on Recent Trends in Engineering and Technology, vol. 8, no. 1, Jan 2013.
  31. Zhang, J., Feng, Z. and Su, Y. 2008. A new approach for detecting copy-move forgery in digital images, 11th IEEE International Conference on Communication Systems, pp. 362-366.
  32. Zhang, Z., Yu, Z. and Su, B. 2010. Detection of composite forged image. IEEE International Conference on Computer Application and System Modeling, vol.11, pp. 572-576.
Index Terms

Computer Science
Information Sciences


Copy-paste forgery Block-based forgery detection techniques Non-overlapping block-based techniques.