Feature extraction and local Zernike moments based geometric invariant watermarking
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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.
KeywordsGeometric 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.
- 4.Cox I, Miller M, Bloom J (2002) Digital watermarking. Morgan-Kaufmann, San FranciscoGoogle 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 489–492Google 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 345–357Google 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: 2418–2431Google Scholar
- 15.Lee HY, Kim H, Lee HK (2006) Robust image watermarking using local invariant features. Opt Eng 45(3):037002-1-037002-11Google Scholar
- 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.Lowe DG (1999) Object recognition from local scale-invariant features. In International Conference on Computer Vision, Corfu, Greece, 1999. pp 1150–1157Google 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 473–476Google 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 861–864Google Scholar
- 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