Journal of Internet Computing and Services (인터넷정보학회논문지)

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

DOI QR Code

Hybrid copy-move-forgery detection algorithm fusing keypoint-based and block-based approaches

특징점 기반 방식과 블록 기반 방식을 융합한 효율적인 CMF 위조 검출 방법

  • Received : 2018.04.13
  • Accepted : 2018.06.11
  • Published : 2018.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

The methods for detecting copy move frogery (CMF) are divided into two categories, block-based methods and keypoint-based methods. Block-based methods have a high computational cost because a large number of blocks should be examined for CMF detection. In addition, the forgery detection may fail if a tampered region undergoes geometric transformation. On the contrary, keypoint-based methods can overcome the disadvantages of the block-based approach, but it can not detect a tampered region if the CMF forgery occurs in the low entropy region of the image. Therefore, in this paper, we propose a method to detect CMF forgery in all areas of image by combining keypoint-based and block-based methods. The proposed method first performs keypoint-based CMF detection on the entire image. Then, the areas for which the forgery check is not performed are selected and the block-based CMF detection is performed for them. Therefore, the proposed CMF detection method makes it possible to detect CMF forgery occurring in all areas of the image. Experimental results show that the proposed method achieves better forgery detection performance than conventional methods.

Copy move frogery(CMF) 위조를 검출하는 기술은 블록(block) 기반 방식과 특징점(keypoint) 기반 방식으로 구분 된다. 블록 기반 방법은 위조 검출 과정에서 조사해야 하는 블록의 수가 많기 때문에 높은 계산 비용이 발생한다. 또한 위조되는 영역이 기하학적 변환을 거친 경우 위조 검출에 실패하는 단점이 있다. 반대로 특징점 기반 접근법은 블록 기반 방식의 단점을 극복 할 순 있지만 CMF 위조가 이미지의 낮은 엔트로피 영역에서 발생하는 경우 검출 할 수 없다는 단점이 존재한다. 따라서 본 논문에서는 특징점 기반 방식과 블록 기반 방식을 융합하여 이미지의 모든 영역에서 CMF 위조를 검출하는 방법을 제안한다. 제안하는 방법은 우선 전체 이미지를 대상으로 특징점 기반 위조 검출을 수행한다. 그 후 위조 검사가 이루어지지 않은 영역을 선별하여 블록 기반 위조 검사를 다시 수행한다. 따라서 제안하는 위조 검출 기술은 이미지의 모든 영역에서 발생하는 CMF 위조를 검출하는 것을 가능하게 해준다. 실험을 통해 제안하는 방법이 기존은 방법보다 우수한 위조 검출 성능을 보이는 것을 확인하였다.

Keywords

References

  1. T. Qazi, K. Hayat, S. U. Khan, et al, "Survey on blind image forgery detection", IET Image Process., Vol. 7, No. 7, pp. 660-670, 2013. http://dx.doi.org/10.1049/iet-ipr.2012.0388
  2. G. K. Birajdar and V. H. Mankar, "Digital image forgery detection using passive techniques: a survey", Digit. Invest., Vol. 10, No. 3, pp. 226-245, 2013. https://doi.org/10.1016/j.diin.2013.04.007
  3. M. D. Ansari, S. P. Ghrera, and V. Tyagi, "Pixel-based image forgery detection: a review", IETE J. Educ., Vol. 55, No. 1, pp. 40-46, 2014. https://doi.org/10.1080/09747338.2014.921415
  4. W. N. N. Diane, S. Xingming, and F. K. Moise, "A survey of partition-based techniques for copy-move forgery detection", Sci. World J., Vol. 55, No. 1, pp. 1-13, 2014. http://dx.doi.org/10.1155/2014/975456
  5. B. Soni, P. K. Das, and D. M. Thounaojam, "CMFD: A detailed review of block based and key feature based techniques in image copy-move forgery detection," IET Image Processing, Vol. 12, No. 2, pp. 167-178, 2017. http://dx.doi.org/10.1049/iet-ipr.2017.0441
  6. A. Fridrich, B. Jessica, David Soukal, and A. Jan Lukas, "Detection of copy-move forgery in digital images", Digital Forensic Research Workshop, 2003. http://dx.doi.org/10.1109/PACIIA.2008.240
  7. B. Mahdian and S. Saic S, "Detection of copy-move forgery using a method based on blur moment invariants", Forensic Sci. Int., Vol. 171, No. 2, pp. 180-189, 2007. https://doi.org/10.1016/j.forsciint.2006.11.002
  8. A. Popescu and H. Farid, "Exposing digital forgeries by detecting duplicated image regions, Department of Computer Science", Dartmouth College, Tech. Rep. TR2004-515, 2004. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.2374
  9. J. Wang, G. Liu, Z. Zhang, Y. Dai, and Z. Wang, "Fast and robust forensics for image Region-Duplication forgery", Acta Automatica Sinica, Vol. 35, No. 12, pp. 1488-1495, 2009. http://dx.doi.org/10.3724/SP.J.1004.2009.01488
  10. Er. S. Khan, and Er. A. Kulkarni, "An efficient method for detection of copy-move forgery using discrete wavelet transform", Int. J. Comput. Sci Eng., Vol. 2, No. 5, pp. 1801-1806, 2010. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.302.3263&rank=1
  11. Y. Huang, W. Lu, W. Sun, D. Long, "Improved DCT-based detection of copy-move forgery in images", Forensic Sci Int., Vol. 206, No. 1, pp. 178-184, 2011. https://doi.org/10.1016/j.forsciint.2010.08.001
  12. S. Ryu, M. Lee, H. Lee, "Detection of copyrotate-move forgery using Zernike moments", Lect. Notes Comput. Sci., Vol. 6387, pp. 51-65, 2010. https://doi.org/10.1007/978-3-642-16435-4_5
  13. C. S. Park, C. Kim, J. Lee, and G. R. Kwon, "Rotation and scale invariant upsampled log-polar fourier descriptor for copy-move forgery detection", Multimedia Tools and Applications, Vol. 75, No. 23, pp. 16577-16595, 2016. https://doi.org/10.1007/s11042-016-3575-z
  14. D. G. Lowe, "Distinctive image features from scaleinvariant keypoints", Int. J. Comput. Vis., Vol. 60, No. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  15. H. Bay, A. Ess, T. Tuytelaars, et al., "Speeded-up robust features (SURF)", Comput. Vis. Image Underst., Vol. 110, No. 3, pp. 346-359, 2008. https://doi.org/10.1016/j.cviu.2007.09.014
  16. J. Zheng, et al. "Fusion of block and keypoints based approaches for effective copy-move image forgery detection", Multidimensional Systems and Signal Processing, Vol. 27, No. 4, pp. 989-1005, 2016. https://doi.org/10.1007/s11045-016-0416-1
  17. V. Christlein, C. Riess, J. Jordan, et al., "An evaluation of popular copymove forgery detection approaches", IEEE Trans. Inf. Forensics Sec., Vol. 7, No. 6, pp. 1841-1854, 2012. https://doi.org/10.1109/TIFS.2012.2218597
  18. Z. Wang, H. Kieu, H. Nguyen, and M. Le, "Digital image correlation in experimental mechanics and image registration in computer vision: similarities, differences and complements", Optics and Lasers in Engineering, Vol. 65, pp. 18-27, 2015. https://doi.org/10.1016/j.optlaseng.2014.04.002
  19. I. Amerini, L. Ballan, R. Caldelli, A. D. Bimbo, L. D. Tongo, and G. Serra, "Copy-move forgery detection and localization by means of robust clustering with J-Linkage", Signal Process. Image Commun., Vol. 28, No. 6, pp. 659-669, 2013. https://doi.org/10.1016/j.image.2013.03.006
  20. X. Pan and S. Lyu, "Region duplication detection using image feature matching", IEEE Trans. Inf. Forensics Secur., Vol. 5, No. 4, pp. 857-867, 2010. https://doi.org/10.1109/TIFS.2010.2078506
  21. I. Amerini, L. Ballan, R. Caldelli, A. D. Bimbo, G. Serra, "A SIFT-based forensic method for copy move attack detection and transformation recovery", IEEE Trans. Inf. Forensics Secur., Vol. 6, No. 3, pp. 1099-1110, 2011. https://doi.org/10.1109/TIFS.2011.2129512
  22. V. Christlein, C. Riess, and E. Angelopoulou, "On Rotation Invariance in Copy-Move Forgery Detection," IEEE Workshop on Information Forensics and Security, 2010. https://doi.org/10.1109/WIFS.2010.5711472