Feature Based Analysis of Copy-Paste Image Tampering Detection

Authors(3) :-Dr. Kusam Sharma, Prof. Pawanesh Abrol, Prof. Devanand

Authentication of a digital image is a challenging task. A tampered image is created by altering some of its contents using standard image processing tools. Copy-paste tampering is created by copying some part of an image and pasting it within the same image for covering unwanted information or an object, is the most used technique in digital image manipulation. The motive of copy-paste tampering detection technique is to locate regions that have been copied and pasted within the same image. A number of techniques are employed to detect copy-paste tampering; using image features / parameters is also one of them. In the present research work, a parametric non-overlapping block-based tampering detection model has been applied to ensure the presence of copy-paste tampering in a given digital image. The behaviour of different parameters has been analysed after their implementation onto a wide variety of digital images having different types, formats and dimensions. Statistical parameters of the input images of three different formats are computed, analysed and compared with those of their tampered images using specific threshold values. The model is tested for three different formats and for seven different selected block sizes. The results show that the proposed model identifies the tampered area for all the given images and works well with low to moderate copy-paste tampering. The results obtained can be used as the initial verification of the images for tampering and to enhance the tampering detection process by identifying most likely cases of possible image tampering. The proposed model is tested with larger domain of images having different types, formats and dimensions and for tampering within an image. However, the model has limitations with certain geometrical transformations.

Authors and Affiliations

Dr. Kusam Sharma
Department of Computer Science & IT, University of Jammu, Jammu, J&K, India
Prof. Pawanesh Abrol
Department of Computer Science & IT, University of Jammu, Jammu, J&K, India
Prof. Devanand
Department of Computer Science & IT, Central University of Jammu, Jammu, J&K, India

Copy-Paste Tampering, Block Based Tampering Detection Techniques, Overlapping Block Based Techniques, Non-Overlapping Block Based Techniques.

  1. Kusam, P. Abrol, and Devanand, "Digital Tampering Detection Techniques: A Review", BVICAM’s International Journal of Information Technology, vol. 1, no. 2, pp. 125-132, 2009.  
  2. X. Pan and S. Lyu, "Region duplication detection using image feature matching", IEEE Transactions of Information Forensics and Security, vol. 5, no. 4, pp. 857-867, Dec. 2010.
  3. 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.
  4. Li Kang and X. Cheng, "Copy-move forgery detection in digital image", IEEE 3rd International Congress on Image and Signal Processing, vol.5, Oct. 2010, pp. 2419-2421.
  5. 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," IEEE International Conference on Multimedia and Expo, Jul. 2007, pp. 1750-1753.        
  6. D. Sharma and P. Abrol, "SVD Based Noise Removal Technique: An Experimental Analysis", International Journal of Advanced Research in Computer Science, vol. 3, no. 5, pp. 214-218,  Sept. – Oct. 2012.
  7. S. Bayram, H. T. Sencar and N. Memon, "An efficient and robust method for detecting copy-move forgery," Proceedings of  the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Apr. 2009, pp. 1053-1056.
  8. H. J. Lin, C.W. Wang and Y.T. Kao, "Fast copy–move forgery detection", WSEAS Transactions on Signal Processing, vol. 5, no. 5, pp. 188–197, 2009.
  9. X. Wang, X. Zhang, Z. Li and S. Wang, "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.
  10. Y. Huang, W. Lu and D. Long, "Improved DCT-based detection of copy-move forgery in images", Forensic Science International, vol. 206, issues 1-3, pp. 178-184, Elsevier, March 2011.
  11. Y. Huang, W. Lu, W. Sun and D. Long, "Improved DCT-based detection of copy-move forgery in images", Forensic Science International, vol. 206, pp.178–184, Elsevier, 2011.
  12. Y. Cao, T. Gao, L. Fan and Q. Yang, "A robust detection algorithm for copy-move forgery in digital images", Forensic Science International, vol. 214, pp.33–43, Elsevier, 2012.
  13. 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, issues 1-3, pp. 158-166, Elsevier, Dec. 2013.
  14. W. Luo, J. Huang, and G. Qiu, "Robust Detection of Region Duplication Forgery in Digital Images", In Proceedings of the 18th International Conference on Pattern Recognition, vol. 4, Aug. 2006, pp. 746-749.
  15. A. C. Popescu and H. Farid, "Exposing digital forgeries by detecting duplicated image regions," Department of Computer Science, Dartmouth College, Tech. Rep. 2004-515, 2004.
  16. J. Zhang, Z. Feng and Y. Su, "A new approach for detecting copy-move forgery in digital images", 11th IEEE International Conference on Communication Systems, Nov. 2008, pp. 362-366.
  17. A. Sharma and P. Abrol, "Research Issues in Designing Improved Eye Gaze Based HCI Techniques for Augmentative and Alternative Communication", International Journal of Emerging Technologies in Computational and Applied Sciences, vol. 6, no. 2, pp. 149-153, September-November 2013.
  18. 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.
  19. M. Tajrobekar. (2014, March 8). "Where must we use variance and mean of image?," [Online]. Available:http://www.researchgate.net/post/ Where_must_we_use_variance_and_mean_of_image.
  20. 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.
  21. D.J. Wheeler. (2011, July 29) "Problems with Skewness and Kurtosis, Part One," [Online]. Available:http://www.qualitydigest.com/inside/ quality-insider-article/problems-skewness-and-kurtosis-part-one.html.
  22. D.J. Wheeler. (2011, January 08) "Problems with Skewness and Kurtosis, Part Two," [Online]. Available:http://www.qualitydigest.com/inside/ quality-insider-article/problems-skewness-and-kurtosis-part-two.html.

Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 555-562
Manuscript Number : CSEIT1726166
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Dr. Kusam Sharma, Prof. Pawanesh Abrol, Prof. Devanand, "Feature Based Analysis of Copy-Paste Image Tampering Detection", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.555-562, November-December-2017. |          | BibTeX | RIS | CSV

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