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Feature extraction and local Zernike moments based geometric invariant watermarking

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Abstract

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.

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References

  1. Abu-Mostafa YS, Psaltis D (1985) Image normalization by complex moments. IEEE Trans Pattern Anal Mach Intell 7(1):46–55

    Article  Google Scholar 

  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 3971:99–109

    Article  Google Scholar 

  3. Bas P, Chassery JM, Macq B (2002) Geometrically invariant watermarking using feature points. IEEE Trans Image Process 11(9):1014–1028

    Article  Google Scholar 

  4. Cox I, Miller M, Bloom J (2002) Digital watermarking. Morgan-Kaufmann, San Francisco

    Google Scholar 

  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 489–492

  6. Dudani SA, Breeding KJ, McGhee RB (1977) Aircraft identification by moment invariants. IEEE Trans Comput 26(1):39–46

    Article  Google Scholar 

  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 345–357

    Google Scholar 

  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):278–286

    Article  Google Scholar 

  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):776–786

    Article  Google Scholar 

  10. Khotanzad A, Hong YH (1990) Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–497

    Article  Google Scholar 

  11. Khotanzad A, Lu JH (1990) Classification of invariant image representations using a neural network. IEEE Trans Acoust Speech Signal Proc 38(6):1028–1038

    Article  Google Scholar 

  12. Kim HS, Lee H-K (2003) Invariant image watermark using Zernike moments. IEEE Trans Circ Syst Video Technol 13(8):766–775

    Article  Google Scholar 

  13. Koenderink JJ (1984) The structure of images. Biol Cybern 50:363–370

    Article  MATH  MathSciNet  Google Scholar 

  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: 2418–2431

  15. Lee HY, Kim H, Lee HK (2006) Robust image watermarking using local invariant features. Opt Eng 45(3):037002-1-037002-11

    Google Scholar 

  16. Lin YT, Huang CY, Lee GC (2011) Rotation, scaling, and translation resilient watermarking for images. IET Image Process 5(4):328–340

    Article  Google Scholar 

  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):767–782

    Article  MATH  Google Scholar 

  18. Lindeberg T (1994) Scale-space theory: a basic tool for analysing structures at different scales. J Appl Stat 21(2):224–270

    Google Scholar 

  19. Lowe DG (1999) Object recognition from local scale-invariant features. In International Conference on Computer Vision, Corfu, Greece, 1999. pp 1150–1157

  20. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  21. O'Ruanaidh J, Pun T (1998) Rotation, scale, and translation invariant digital image watermarking. Signal Process 66(3):303–317

    Article  MATH  Google Scholar 

  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):241–251

    Article  Google Scholar 

  23. Petitcolas FAP (2000) Watermarking schemes evaluation. IEEE Signal Process Mag 17(5):58–64

    Article  Google Scholar 

  24. Reddi SS (1981) Radial and angular moment invariants for image identification. IEEE Trans Pattern Anal Mach Intell 3(2):240–242

    Article  Google Scholar 

  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):937–943

    Article  Google Scholar 

  26. Seo JS, Yoo CD (2004) Localized image watermarking based on feature points of scale-space representation. Pattern Recogn 37(7):1365–1375

    Article  MATH  Google Scholar 

  27. Seo JS, Yoo CD (2006) Image watermarking based on invariant regions of scale-space representation. IEEE Trans Signal Process 54(4):1537–1549

    Article  Google Scholar 

  28. Tang CW, Hang HM (2003) A feature-based robust digital image watermarking scheme. IEEE Trans Signal Process 51(4):950–959

    Article  MathSciNet  Google Scholar 

  29. Teh CH, Chin RT (1988) On image analysis by the methods of moments. IEEE Trans Pattern Anal Mach Intell 10(4):496–513

    Article  MATH  Google Scholar 

  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):735–743

    Article  MathSciNet  Google Scholar 

  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):1431–1445

    Article  Google Scholar 

  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 473–476

  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):1605–1616

    Article  MATH  MathSciNet  Google Scholar 

  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 861–864

  35. Yuan XC, Pun CM (2012) Geometrically invariant image watermarking based on feature extraction and Zernike transform. Int J Secur Appl 6(2):217–222

    Google Scholar 

  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):2189–2199

    Article  MathSciNet  Google Scholar 

  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

    Article  Google Scholar 

  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):1055–1068

    Article  MathSciNet  Google Scholar 

  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):753–765

    Article  Google Scholar 

Download references

Acknowledgments

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.

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Macau, Macau SAR, China

    Xiao-Chen Yuan & Chi-Man Pun

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  1. Xiao-Chen Yuan
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Correspondence to Xiao-Chen Yuan.

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Yuan, XC., Pun, CM. Feature extraction and local Zernike moments based geometric invariant watermarking. Multimed Tools Appl 72, 777–799 (2014). https://doi.org/10.1007/s11042-013-1405-0

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