Multi-scale feature extraction and adaptive matching for copy-move forgery detection

Article

Abstract

A copy-move forgery detection scheme by using multi-scale feature extraction and adaptive matching is proposed in this paper. First, the host image is segmented into the non-overlapping patches of irregular shape in different scales. Then, Scale Invariant Feature Transform is applied to extract feature points from all patches, to generate the multi-scale features. An Adaptive Patch Matching algorithm is subsequently proposed for finding the matching that indicate the suspicious forged regions in each scale. Finally, the suspicious regions in all scales are merged to generate the detected forgery regions in the proposed Matched Keypoints Merging algorithm. Experimental results show that the proposed scheme performs much better than the existing state-of-the-art copy-move forgery detection algorithms, even under various challenging conditions, including the geometric transforms, such as scaling and rotation, and the common signal processing, such as JPEG compression and noise addition; in addition, the special cases such as the multiple copies and the down-sampling are also evaluated, the results indicate the very good performance of the proposed scheme.

Keywords

Copy-Move Forgery Detection Multi-Scale Feature Extraction Adaptive Patch Matching 

References

  1. 1.
    Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282. doi:10.1109/TPAMI.2012.120 CrossRefGoogle Scholar
  2. 2.
    Amerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G (2011) A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE Trans Inf Forensics Secur 6(3):1099–1110CrossRefGoogle Scholar
  3. 3.
    Amerini I, Ballan L, Caldelli R, Del Bimbo A, Del Tongo L, Serra G (2013) Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Process Image Commun 28(6):659–669CrossRefGoogle Scholar
  4. 4.
    Bay H, Tuytelaars T, Van Gool L (2006) Surf: Speeded up robust features. In: Computer Vision–ECCV 2006. Springer, pp 404-417Google Scholar
  5. 5.
    Bayram S, Sencar HT, Memon N (2009) An efficient and robust method for detecting copy-move forgery. In: Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, IEEE, pp 1053–1056Google Scholar
  6. 6.
    Bi XL, Pun CM, Yuan XC (2016) Multi-Level Dense Descriptor and Hierarchical Feature Matching for Copy–Move Forgery Detection. Inf Sci 345:226–242CrossRefGoogle Scholar
  7. 7.
    Bravo-Solorio S, Nandi AK (2011) Exposing duplicated regions affected by reflection, rotation and scaling. In: Acoustics, Speech and Signal Processing (ICASSP), 2011 I.E. International Conference on, IEEE, pp 1880–1883Google Scholar
  8. 8.
    Bravo-Solorio S, Nandi AK (2012) Exposing postprocessed copy–paste forgeries through transform-invariant features. IEEE Trans Inf Forensics Secur 7(3):1018–1028CrossRefGoogle Scholar
  9. 9.
    Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans Inf Forensics Secur 7(6):1841–1854. doi:10.1109/Tifs.2012.2218597 CrossRefGoogle Scholar
  10. 10.
    Costanzo A, Amerini I, Caldelli R, Barni M (2014) Forensic analysis of sift keypoint removal and injection. IEEE Trans Inf Forensics Secur 9(Sept.2014):1450–1464CrossRefGoogle Scholar
  11. 11.
    Emam M, Han Q, Niu X (2015) PCET based copy-move forgery detection in images under geometric transforms. Multimed Tools Appl:1–15. doi:10.1007/s11042-015-2872-2
  12. 12.
    Fridrich J, Soukal D, Lukáš J (2003) Detection of copy-move forgery in digital images. In: In Proceedings of Digital Forensic Research Workshop, CiteseerGoogle Scholar
  13. 13.
    Huang H, Guo W, Zhang Y (2008) Detection of copy-move forgery in digital images using SIFT algorithm. In: Computational Intelligence and Industrial Application, 2008. PACIIA'08. Pacific-Asia Workshop on, IEEE, pp 272–276Google Scholar
  14. 14.
    Kang X, Wei S (2008) Identifying tampered regions using singular value decomposition in digital image forensics. In: Computer Science and Software Engineering, 2008 International Conference on, IEEE, pp 926–930Google Scholar
  15. 15.
    Li G, Wu Q, Tu D, Sun S (2007) A sorted neighborhood approach for detecting duplicated regions in image forgeries based on DWT and SVD. In: Multimedia and Expo, 2007 I.E. International Conference on, IEEE, pp 1750–1753Google Scholar
  16. 16.
    Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518CrossRefGoogle Scholar
  17. 17.
    Lin H, Wang C, Kao Y (2009) Fast copy-move forgery detection. WSEAS. Trans Signal Process 5(5):188–197Google Scholar
  18. 18.
    Lowe DG (1999) Object recognition from local scale-invariant features. In: Computer vision, 1999. The proceedings of the seventh IEEE international conference on, IEEE, pp 1150–1157Google Scholar
  19. 19.
    Luo W, Huang J, Qiu G (2006) Robust detection of region-duplication forgery in digital image. In: Pattern Recognition, 2006. ICPR 2006. 18th International Conference on, IEEE, pp 746–749Google Scholar
  20. 20.
    Mahdian B, Saic S (2007) Detection of copy–move forgery using a method based on blur moment invariants. Forensic Sci Int 171(2):180–189CrossRefGoogle Scholar
  21. 21.
    Pan XY, Lyu S (2010) region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–867. doi:10.1109/Tifs.2010.2078506 CrossRefGoogle Scholar
  22. 22.
    Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. Dept Comput Sci, Dartmouth College, Tech Rep TR2004–515Google Scholar
  23. 23.
    Pun CM, Yuan XC, Bi XL (2015) Image forgery detection using adaptive over-segmentation and feature points matching. IEEE Trans Inf Forensics Secur 10(8):1705–1716CrossRefGoogle Scholar
  24. 24.
    Ryu SJ, Kirchner M, Lee MJ, Lee HK (2013) Rotation invariant localization of duplicated image regions based on zernike moments. IEEE Trans Inf Forensics Secur 8(8):1355–1370. doi:10.1109/Tifs.2013.2272377 CrossRefGoogle Scholar
  25. 25.
    Sekhar R, Shaji RS (2016) A study on segmentation-based copy-move forgery detection using DAISY descriptor. In Proceedings of the International Conference on Soft Computing Systems. Springer, India, pp 223–233Google Scholar
  26. 26.
    Shivakumar B, Baboo LDSS (2011) Detection of region duplication forgery in digital images using SURF. IJCSI International Journal of Computer Science Issues 8(4):199–205Google Scholar
  27. 27.
    Wang J, Liu G, Li H, Dai Y, Wang Z (2009a) Detection of image region duplication forgery using model with circle block. In: Multimedia Information Networking and Security, 2009. MINES'09. International Conference on, IEEE, pp 25–29Google Scholar
  28. 28.
    Wang J, Liu G, Zhang Z, Dai Y, Wang Z (2009b) Fast and robust forensics for image region-duplication forgery. Acta Automat Sin 35(12):1488–1495CrossRefGoogle Scholar
  29. 29.
    Xu B, Wang J, Liu G, Dai Y (2010) Image copy-move forgery detection based on SURF. In: Multimedia Information Networking and Security (MINES), 2010 International Conference on, IEEE, pp 889–892Google Scholar
  30. 30.
    Yu L, Han Q, Niu X (2014) Feature point-based copy-move forgery detection: covering the non-textured areas. Multimed Tools Appl 75(2):1159–1176. doi:10.1007/s11042-014-2362-y CrossRefGoogle Scholar
  31. 31.
    Zhu Y, Shen X, Chen H (2015) Copy-move forgery detection based on scaled ORB. Multimed Tools Appl 75(6):3221–3233. doi:10.1007/s11042-014-2431-2 MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer and Information ScienceUniversity of MacauMacauChina
  2. 2.Faculty of Information TechnologyMacau University of Science and TechnologyMacauChina

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