KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Detecting Copy-move Forgeries in Images Based on DCT and Main Transfer Vectors

  • Zhang, Zhi (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Wang, Dongyan (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Wang, Chengyou (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Zhou, Xiao (School of Mechanical, Electrical and Information Engineering, Shandong University)
  • Received : 2017.01.23
  • Accepted : 2017.05.26
  • Published : 2017.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

With the growth of the Internet and the extensive applications of image editing software, it has become easier to manipulate digital images without leaving obvious traces. Copy-move is one of the most common techniques for image forgery. Image blind forensics is an effective technique for detecting tampered images. This paper proposes an improved copy-move forgery detection method based on the discrete cosine transform (DCT). The quantized DCT coefficients, which are feature representations of image blocks, are truncated using a truncation factor to reduce the feature dimensions. A method for judging whether two image blocks are similar is proposed to improve the accuracy of similarity judgments. The main transfer vectors whose frequencies exceed a threshold are found to locate the copied and pasted regions in forged images. Several experiments are conducted to test the practicability of the proposed algorithm using images from copy-move databases and to evaluate its robustness against post-processing methods such as additive white Gaussian noise (AWGN), Gaussian blurring, and JPEG compression. The results of experiments show that the proposed scheme effectively detects both copied region and pasted region of forged images and that it is robust to the post-processing methods mentioned above.

Keywords

References

  1. G. K. Birajdar and V. H. Mankar, "Digital image forgery detection using passive techniques: A survey," Digital Investigation, vol. 10, no. 3, pp. 226-245, Oct. 2013. https://doi.org/10.1016/j.diin.2013.04.007
  2. Photo Tampering throughout History, the pictures of Senator John Kerry and Jane Fonda.
  3. Photo Tampering throughout History, the actual and doctored images of Iranian missile test.
  4. W. C. Hu, W. H. Chen, D. Y. Huang, and C. Y. Yang, "Effective image forgery detection of tampered foreground or background image based on image watermarking and alpha mattes," Multimedia Tools and Applications, vol. 75, no. 6, pp. 3495-3516, Mar. 2016. https://doi.org/10.1007/s11042-015-2449-0
  5. Y. Dai and S. H. Su, "A diploma anti-forgery system based on lightweight digital signatures," in Proc. of the 10th Inernational Conference on Computational Intelligence and Security, Kunming, China, Nov. 15-16, pp. 647-651, 2014.
  6. J. Fridrich, D. Soukalm, and J. Lukas, "Detection of copy-move forgery in digital images," in Proc. of the Digital Forensic Research Workshop, Cleveland, OH, USA, 10 pages, Aug. 5-8, 2003.
  7. A. C. Popescu and H. Farid, "Exposing digital forgeries by detecting duplicated image regions," Technical Report, TR 2004-515, Deparment of Computer Science, Dartmouth College, 2004.
  8. X. B. Kang and S. M. Wei, "Identifying tampered regions using singular value decomposition in digital image forensics," in Proc. of the International Conference on Computer Science and Software Engineering, Wuhan, China, Dec. 12-14, 2008, pp. 926-930.
  9. J. Zhao and J. C. 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, Dec. 2013. https://doi.org/10.1016/j.forsciint.2013.09.013
  10. Y. P. Huang, W. Lu, W. Sun, and D. Y. Long, "Improved DCT-based detection of copy-move forgery in images," Forensic Science International, vol. 206, no. 1-3, pp. 178-184, Mar. 2011. https://doi.org/10.1016/j.forsciint.2010.08.001
  11. S. Bayram, H. T. Sencar, and N. Memon, "An efficient and robust method for detecting copy-move forgery," in Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, pp. 1053-1056, Apr. 19-24, 2009.
  12. I. Amerini, M. Barni, R. Caldelli, and A. Costanzo, "Removal and injection of keypoints for SIFT-based copy-move counter-forensics," EURASIP Journal on Information Security, vol. 2013, article number: 8, 12 pages, Dec. 2013.
  13. B. Xu, J. W. Wang, G. J. Liu, and Y. W. Dai, "Image copy-move forgery detection based on SURF", in Proc. of the 2nd International Conference on Multimedia Information Networking and Security, Nanjing, China, pp. 889-892, Nov. 4-6, 2010.
  14. R. C. Pandey, R. Agrawal, S. K. Singh, and K. K. Shukla, "Passive copy move forgery detection using SURF, HOG and SIFT features," in Proc. of the 3rd International Conference on Frontiers in Intelligent Computing: Theory and Applications, Reykjavik, Iceland, pp. 659-666, Nov. 14-15, 2014.
  15. 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, Sept. 2007. https://doi.org/10.1016/j.forsciint.2006.11.002
  16. A. V. Malviya and S. A. Ladhake, "Pixel based image forensic technique for copy-move forgery detection using auto color correlogram," in Proc. of the 7th International Conference on Communication, Computing and Virtualization, Mumbai, India, pp. 383-390, Feb. 26-27, 2016.
  17. F. Yang, J. W. Li, W. Lu, and J. Weng, "Copy-move forgery detection based on hybrid features," Engineering Applications of Artificial Intelligence, vol. 59, pp. 73-83, Mar. 2017. https://doi.org/10.1016/j.engappai.2016.12.022
  18. D. Vaishnavi and T. S. Subashini, "A passive technique for image forgery detection using contrast context histogram features," International Journal of Electronic Security and Digital Forensics, vol. 7, no. 3, pp. 278-289, Jul. 2015. https://doi.org/10.1504/IJESDF.2015.070394
  19. W. Q. Luo, J. W. Huang, and G. P. Qiu, "Robust detection of region-duplication forgery in digital image," in Proc. of the 18th International Conference on Pattern Recognition, Hong Kong, China, vol. 4, pp. 746-749, Aug. 20-24, 2006.
  20. Q. M. Fu, X. Zhou, C. Y. Wang, and B. C. Jiang, "Mathematical relation between APBT-based and DCT-based JPEG image compression schemes," Journal of Communications, vol. 11, no. 1, pp. 84-92, Jan. 2016.
  21. T. T. Ng and S. F. Chang, "A data set of authentic and spliced image blocks," ADVENT Technical Report, 203-2004-3, Columbia University, Jun. 2004.
  22. D. Tralic, I. Zupancic, S. Grgic, and M. Grgic, "CoMoFoD - New database for copy-move forgery detection," in Proc. of the 55th International Symposium ELMAR, Zadar, Croatia, pp. 49-54, Sept. 25-27, 2013.
  23. V. Christlein, C. Riess, J. Jordan, C. Riess, and E. Angelopoulou, "An evaluation of popular copy-move forgery detection approaches," IEEE Transactions on Information Forensics and Security, vol. 7, no. 6, pp. 1841-1854, Dec. 2012. https://doi.org/10.1109/TIFS.2012.2218597

Cited by

  1. Enhanced block-based copy-move forgery detection using k-means clustering vol.30, pp.4, 2017, https://doi.org/10.1007/s11045-018-0624-y
  2. A passive technique for detecting copy-move forgeries by image feature matching vol.79, pp.43, 2017, https://doi.org/10.1007/s11042-020-09655-2