Multimedia Tools and Applications

, Volume 72, Issue 1, pp 777–799 | Cite as

Feature extraction and local Zernike moments based geometric invariant watermarking

  • Xiao-Chen Yuan
  • Chi-Man Pun


A robust and geometric invariant digital image watermarking scheme based on robust feature detector and local Zernike transform is proposed in this paper. The robust feature extraction method is proposed based on the Scale Invariant Feature Transform (SIFT) algorithm, to extract circular regions/patches for watermarking use. Then a local Zernike moments-based watermarking scheme is raised, where the watermarked regions/patches can be obtained directly by inverse Zernike Transform. Each extracted circular patch is decomposed into a collection of binary patches and Zernike transform is applied to the appointed binary patches. Magnitudes of the local Zernike moments are calculated and modified to embed the watermarks. Experimental results show that the proposed watermarking scheme is very robust against geometric distortion such as rotation, scaling, cropping, and affine transformation; and common signal processing such as JPEG compression, median filtering, and low-pass Gaussian filtering.


Geometric invariant Feature extraction SIFT Local Zernike transform Inverse Zernike transform 



The authors would like to thank the referees for their valuable comments. This work was supported in part by the Science and Technology Development Fund of Macau SAR (Project No. 034/2010/A2) and the Research Committee of the University of Macau.


  1. 1.
    Abu-Mostafa YS, Psaltis D (1985) Image normalization by complex moments. IEEE Trans Pattern Anal Mach Intell 7(1):46–55CrossRefGoogle Scholar
  2. 2.
    Bas P, Chassery JM, Macq B (2000) Robust watermarking based on the warping of pre-defined triangular patterns. Proc SPIE Secur Watermark Multimed Contents 3971:99–109CrossRefGoogle Scholar
  3. 3.
    Bas P, Chassery JM, Macq B (2002) Geometrically invariant watermarking using feature points. IEEE Trans Image Process 11(9):1014–1028CrossRefGoogle Scholar
  4. 4.
    Cox I, Miller M, Bloom J (2002) Digital watermarking. Morgan-Kaufmann, San FranciscoGoogle Scholar
  5. 5.
    Dong P, Galatsanos NP (2002) Affine transformation resistant watermarking based on image normalization. In: Image processing. Proceedings. 2002 International Conference on, 24–28 June 2002. pp 489–492Google Scholar
  6. 6.
    Dudani SA, Breeding KJ, McGhee RB (1977) Aircraft identification by moment invariants. IEEE Trans Comput 26(1):39–46CrossRefGoogle Scholar
  7. 7.
    Filipe J, Obaidat MS, Scagliola M, Guccione P (2009) Geometric distortion resilient watermarking based on a single robust feature for still images. In: e-business and telecommunications, vol 48. Communications in computer and information science. Springer, Berlin, pp 345–357Google Scholar
  8. 8.
    Gao XB, Deng C, Li XL, Tao DC (2010) Geometric distortion insensitive image watermarking in affine covariant regions. IEEE Trans Syst Man Cybern Part C Appl Rev 40(3):278–286CrossRefGoogle Scholar
  9. 9.
    Kang XG, Huang JW, Yun QS, Yan L (2003) A DWT-DFT composite watermarking scheme robust to both affine transform and JPEG compression. IEEE Trans Circ Syst Video Technol 13(8):776–786CrossRefGoogle Scholar
  10. 10.
    Khotanzad A, Hong YH (1990) Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–497CrossRefGoogle Scholar
  11. 11.
    Khotanzad A, Lu JH (1990) Classification of invariant image representations using a neural network. IEEE Trans Acoust Speech Signal Proc 38(6):1028–1038CrossRefGoogle Scholar
  12. 12.
    Kim HS, Lee H-K (2003) Invariant image watermark using Zernike moments. IEEE Trans Circ Syst Video Technol 13(8):766–775CrossRefGoogle Scholar
  13. 13.
    Koenderink JJ (1984) The structure of images. Biol Cybern 50:363–370CrossRefMATHMathSciNetGoogle Scholar
  14. 14.
    Lee HY, Kang IK, Lee HK, Suh YH (2005) Evaluation of feature extraction techniques for robust watermarking. In: The 4th International Workshop on Digital Watermarking, Siena, Italy, September 15–17 2005. Lecture Notes in Computer Science 3710, Springer 2005: 2418–2431Google Scholar
  15. 15.
    Lee HY, Kim H, Lee HK (2006) Robust image watermarking using local invariant features. Opt Eng 45(3):037002-1-037002-11Google Scholar
  16. 16.
    Lin YT, Huang CY, Lee GC (2011) Rotation, scaling, and translation resilient watermarking for images. IET Image Process 5(4):328–340CrossRefGoogle Scholar
  17. 17.
    Lin CY, Wu M, Bloom JA, Cox IJ, Miller ML, Lui YM (2001) Rotation, scale, and translation resilient watermarking for images. IEEE Trans Image Process 10(5):767–782CrossRefMATHGoogle Scholar
  18. 18.
    Lindeberg T (1994) Scale-space theory: a basic tool for analysing structures at different scales. J Appl Stat 21(2):224–270Google Scholar
  19. 19.
    Lowe DG (1999) Object recognition from local scale-invariant features. In International Conference on Computer Vision, Corfu, Greece, 1999. pp 1150–1157Google Scholar
  20. 20.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  21. 21.
    O'Ruanaidh J, Pun T (1998) Rotation, scale, and translation invariant digital image watermarking. Signal Process 66(3):303–317CrossRefMATHGoogle Scholar
  22. 22.
    Perantonis SJ, Lisboa PJG (1992) Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers. IEEE Trans Neural Netw 3(2):241–251CrossRefGoogle Scholar
  23. 23.
    Petitcolas FAP (2000) Watermarking schemes evaluation. IEEE Signal Process Mag 17(5):58–64CrossRefGoogle Scholar
  24. 24.
    Reddi SS (1981) Radial and angular moment invariants for image identification. IEEE Trans Pattern Anal Mach Intell 3(2):240–242CrossRefGoogle Scholar
  25. 25.
    Reeves AP, Prokop RJ, Andrews SE, Kuhl FP (1988) Three-dimensional shape analysis using moments and Fourier descriptors. IEEE Trans Pattern Anal Mach Intell 10(6):937–943CrossRefGoogle Scholar
  26. 26.
    Seo JS, Yoo CD (2004) Localized image watermarking based on feature points of scale-space representation. Pattern Recogn 37(7):1365–1375CrossRefMATHGoogle Scholar
  27. 27.
    Seo JS, Yoo CD (2006) Image watermarking based on invariant regions of scale-space representation. IEEE Trans Signal Process 54(4):1537–1549CrossRefGoogle Scholar
  28. 28.
    Tang CW, Hang HM (2003) A feature-based robust digital image watermarking scheme. IEEE Trans Signal Process 51(4):950–959CrossRefMathSciNetGoogle Scholar
  29. 29.
    Teh CH, Chin RT (1988) On image analysis by the methods of moments. IEEE Trans Pattern Anal Mach Intell 10(4):496–513CrossRefMATHGoogle Scholar
  30. 30.
    Tsai JS, Huang WB, Kuo YH (2011) On the selection of optimal feature region set for robust digital image watermarking. IEEE Trans Image Process 20(3):735–743CrossRefMathSciNetGoogle Scholar
  31. 31.
    Tsai JS, Huang WB, Kuo YH, Horng MF (2012) Joint robustness and security enhancement for feature-based image watermarking using invariant feature regions. Signal Process 92(6):1431–1445CrossRefGoogle Scholar
  32. 32.
    Viet QP, Miyaki T, Yamasaki T, Aizawa K (2007) Geometrically invariant object-based watermarking using SIFT feature. In Image processing. ICIP 2007. IEEE International Conference on, Sept. 16–Oct. 19 2007, pp 473–476Google Scholar
  33. 33.
    Wang XY, Meng L, Yang HY (2009) Geometrically invariant color image watermarking scheme using feature points. Sci China Ser F: Inform Sci 52(9):1605–1616CrossRefMATHMathSciNetGoogle Scholar
  34. 34.
    Xin Y, Liao S, Pawlak M (2004) A multibit geometrically robust image watermark based on Zernike moments. In: Pattern recognition. ICPR 2004. Proceedings of the 17th International Conference on, 23–26 Aug. 2004. pp 861–864Google Scholar
  35. 35.
    Yuan XC, Pun CM (2012) Geometrically invariant image watermarking based on feature extraction and Zernike transform. Int J Secur Appl 6(2):217–222Google Scholar
  36. 36.
    Zhang H, Shu H, Coatrieux G, Zhu J, Wu QM, Zhang Y, Zhu H, Luo L (2011) Affine Legendre moment invariants for image watermarking robust to geometric distortions. IEEE Trans Image Process 20(8):2189–2199CrossRefMathSciNetGoogle Scholar
  37. 37.
    Zheng D, Liu Y, Zhao J, Saddik AE (2007) A survey of RST invariant image watermarking algorithms. ACM Comput Surv 39(2):5. doi: 10.1145/1242471.1242473 CrossRefGoogle Scholar
  38. 38.
    Zheng D, Wang S, Zhao JY (2009) RST invariant image watermarking algorithm with mathematical modeling and analysis of the watermarking processes. IEEE Trans Image Process 18(5):1055–1068CrossRefMathSciNetGoogle Scholar
  39. 39.
    Zheng D, Zhao J, El Saddik A (2003) RST-invariant digital image watermarking based on log-polar mapping and phase correlation. IEEE Trans Circ Syst Video Technol 13(8):753–765CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computer and Information ScienceUniversity of MacauMacau SARChina

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