Multimedia Tools and Applications

, Volume 75, Issue 23, pp 16577–16595 | Cite as

Rotation and scale invariant upsampled log-polar fourier descriptor for copy-move forgery detection

  • Chun-Su Park
  • Changjae Kim
  • Jihoon Lee
  • Goo-Rak Kwon


Digital image forgery is becoming increasingly popular with the rapid progress of digital media editing tools. Copy-move forgery (CMF) is one of the most common methods of digital image forgery. For CMF detection (CMFD), we propose an upsampled log-polar Fourier (ULPF) descriptor that is robust to several geometric transformations including rotation, scaling, sheering, and reflection. We first describe the theoretical background of the ULPF representation. Then, we propose a feature extraction algorithm that can extract rotation and scale invariant features from the ULPF representation. In addition, we analyze the common CMFD processing pipeline and improve a part of processing pipeline to efficiently handle various types of tampering attacks. In our simulation, we present comparative results between the proposed feature descriptor and state-of-the-art ones with proven performance guarantees.


Digital image forgery Copy-move forgery Upsampled log-polar Fourier features Common processing pipeline 


  1. 1.
    Alahi A, Ortiz R, Vandergheynst P (2012) FREAK: Fast Retina keypoint, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 16–21Google Scholar
  2. 2.
    Amerini I, Ballan L, Caldelli R, Bimbo AD, 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, Bimbo AD, Tongo LD, 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, Ess A, Tuytelaars T, Gool LV (2008) SURF: Speeded Up robust features. Comput Vis Image Understand 110(3):346–359CrossRefGoogle Scholar
  5. 5.
    Bayram S, Sencar HT, Memon N (2009) An efficient and robust method for detecting copy-move forgery, IEEE International Conference on Acoustics, Speech and Signal Processing, 1053–1056Google Scholar
  6. 6.
    Beis JS, Lowe DG (1997) Shape indexing using approximate Nearest-Neighbour search in High-Dimensional spaces, IEEE Conference on Computer Vision and Pattern Recognition, 1000–1006Google Scholar
  7. 7.
    Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10(3):226–245CrossRefGoogle Scholar
  8. 8.
    Cao Y, Gao T, Fan L, Yang Q (2012) A robust detection algorithm for copy-move forgery in digital images. Forensic Sci Int 214(1):33–43CrossRefGoogle Scholar
  9. 9.
    Chapman B, Jost G, Pas RVD (2008) Using openMP: portable shared memory parallel programming MIT pressGoogle Scholar
  10. 10.
    Chen C, Ni J, Huang J (2013) Blind detection of median filtering in digital images: a difference domain based approach. IEEE Trans Image Process 22(2):4699–4710MathSciNetCrossRefGoogle Scholar
  11. 11.
    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):1841–1854CrossRefGoogle Scholar
  12. 12.
    Christlein V, Riess C, Angelopoulou E (2010) On rotation invariance in copy-move forgery detection, IEEE International Workshop on Information Forensics and Security, 1–6Google Scholar
  13. 13.
    Farid H (2009) Image forgery detection. IEEE Signal Proc Mag 26(2):16–25CrossRefGoogle Scholar
  14. 14.
    Fridrich AJ, Soukal BD, Lukas A.J (2003) Detection of copy-move forgery in digital images, Digital Forensic Research WorkshopGoogle Scholar
  15. 15.
    Huang Y, Lu W, Sun W, Long D (2011) Improved DCT-based detection of copy-move forgery in images. Forensic Sci Int 206(1):178–184CrossRefGoogle Scholar
  16. 16.
    Kang X, Wei S (2008) Identifying tampered regions using singular value decomposition in digital image forensics. Int Conf Comput Sci Soft Eng 3:926–930Google Scholar
  17. 17.
    Khan Er. S, Kulkarni Er. A (2010) An efficient method for detection of copy-move forgery using discrete wavelet transform. Int J Comput Sci Eng 2(5):1801–1806Google Scholar
  18. 18.
    Kirchner M, Schttle P, Riess C (2015) Thinking beyond the block: block matching for copy-move forgery detection revisited, Media Watermarking, Security, and ForensicsGoogle Scholar
  19. 19.
    Kwon GR, Lama RK, Pyun JY, Park CS (2015) Multimedia digital rights management based on selective encryption for flexible business model, Multimedia Tools and Applications. doi:10.1007/s11042-015-2563-z
  20. 20.
    Langille A, Gong M (2006) An Efficient Match-based Duplication Detection Algorithm, Canadian Conference on Computer and Robot Vision, 64–71Google Scholar
  21. 21.
    Leutenegger S, Stefan M (2011) BRISK: Binary Robust invariant scalable keypoints, IEEE International Conference on Computer Vision (ICCV), 2548–2555Google Scholar
  22. 22.
    Li W, Yu N (2010) Rotation robust detection of copy-move forgery, IEEE International Conference on Image Processing, 2113–2116Google Scholar
  23. 23.
    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
  24. 24.
    Li L, Li S, Zhu H, Chu SC, Roddick JF, Pan JS (2013) An efficient scheme for detecting copy-move forged images by local binary patterns. J Inf Hiding and Multimedia Signal Process 4(1):46–56Google Scholar
  25. 25.
    Lin H, Wang C, Kao Y (2009) Fast copy-move forgery detection. WSEAS Trans Signal Process 5(5):188–197Google Scholar
  26. 26.
    Lin HJ, Wang CW, Kao YT (2009) Fast copy-move forgery detection. WSEAS Trans Signal Process 5(5):188–197Google Scholar
  27. 27.
    Liu W, Zhang H, Tao D, Wang Y, Lu K (2016) Large-scale paralleled sparse principal component analysis. Multimedia Tools Appl 75(3):1481–1493CrossRefGoogle Scholar
  28. 28.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  29. 29.
    Luo Y, Tao D, Geng B, Xu C, Maybank S (2013) Manifold regularized multitask learning for semi-supervised multilabel image classification. IEEE Trans Image Process 22(2):523–536MathSciNetCrossRefGoogle Scholar
  30. 30.
    Luo Y, Wen Y, Tao D, Gui J, Xu C (2016) Large margin Multi-Modal Multi-Task feature extraction for image classification. IEEE Trans Image Process 25 (1):414–427MathSciNetCrossRefGoogle Scholar
  31. 31.
    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
  32. 32.
    Muhammad G, Hussain M, Bebis G (2012) Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digit Investig 9(1):49–57CrossRefGoogle Scholar
  33. 33.
    Murali S, Chittapur GB, S 1 PH, Anami BS (2012) Comparison and analysis of photo image forgery detection techniques. Int J Comput Sci & Appl 2(6):45–56Google Scholar
  34. 34.
    Oppenheim AV, Schafer RW, Buck JR (1999) Discrete-time signal processing, 2nd. Prentice-Hall, Englewood-Cliffs, NJGoogle Scholar
  35. 35.
    Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–867CrossRefGoogle Scholar
  36. 36.
    Popescu A, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions, Department of Computer Science, Dartmouth College, Tech. Rep. TR2004-515Google Scholar
  37. 37.
    Ryu S, Lee M, Lee H (2010) Detection of copy-rotate-move forgery using Zernike moments. Lect Notes Comput Sci 6387:51–65CrossRefGoogle Scholar
  38. 38.
    Schneider M, Chang S (1996) A robust content based digital signature for image authentication. Int Conf Image Process (ICIP) 3:227–230Google Scholar
  39. 39.
    Sekhar R, Shaji RS (2016) A study on segmentation-based copy-move forgery detection using DAISY descriptor, Proceedings of the International Conference on Soft Computing Systems, 223–233Google Scholar
  40. 40.
    Sorensen HV, etal (1987) Real-valued fast fourier transform algorithms. IEEE Trans Acoust Speech Signal Process 35(6):849–863CrossRefGoogle Scholar
  41. 41.
    Wang J, Liu G, Zhang Z, Dai Y, Wang Z (2009) Fast and robust forensics for image Region-Duplication forgery. Acta Automatica Sinica 35(12):1488–1495CrossRefGoogle Scholar
  42. 42.
    Wu Q, Wang S, Zhang X (2010) Detection of image region-duplication with rotation and scaling tolerance. Lect Notes Comput Sci 6421:100–108CrossRefGoogle Scholar
  43. 43.
    Yaroslavsky L, Ye Wang DFT (2000) DCT, MDCT, DST and signal fourier spectrum analysis, Signal Processing Conference, 1–4Google Scholar
  44. 44.
    Yeung M, Mintzer F (1997) An Invisible Watermarking Technique for Image Verification, International Conference on Image Processing (ICIP), vol. 2, pp 690–683Google Scholar
  45. 45.

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Chun-Su Park
    • 1
  • Changjae Kim
    • 2
  • Jihoon Lee
    • 3
  • Goo-Rak Kwon
    • 4
  1. 1.Department of SoftwareSejong UniversitySeoulSouth Korea
  2. 2.Department of Civil and Environmental EngineeringMyongji UniversityYonginSouth Korea
  3. 3.Department of Information and Communication EngineeringSangmyung UniversityCheonanSouth Korea
  4. 4.Department of Information and Communication EngineeringChosun UniversityGwangjuSouth Korea

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