Invariant Image Watermarking Using Accurate Zernike Moments

Problem statement: Digital image watermarking is the most popular method for image authentication, copyright protection and content description. Zernike moments are the most widely used moments in image processing and pattern recognition. The magnitudes of Zernike moments are rotation invariant so they can be used just as a watermark signal or be further modified to carry embedded data. The computed Zernike moments in Cartesian coordinate are not accurate due to geometrical and numerical error. Approach: In this study, we employed a robust image-watermarking algorithm using accurate Zernike moments. These moments are computed in polar coordinate, where both approximation and geometric errors are removed. Accurate Zernike moments are used in image watermarking and proved to be robust against different kind of geometric attacks. The performance of the proposed algorithm is evaluated using standard images. Results: Experimental results show that, accurate Zernike moments achieve higher degree of robustness than those approximated ones against rotation, scaling, flipping, JPEG compression and affine transformation. Conclusion: By computing accurate Zernike moments, the embedded bits watermark can be extracted at low error rate.

[1]  Khalid M. Hosny,et al.  Fast computation of accurate Zernike moments , 2008, Journal of Real-Time Image Processing.

[2]  Miroslaw Pawlak,et al.  Accurate Computation of Zernike Moments in Polar Coordinates , 2007, IEEE Transactions on Image Processing.

[3]  Roland T. Chin,et al.  On image analysis by the methods of moments , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Heung-Kyu Lee,et al.  Rotation-, scale-, and translation-invariant image watermark using higher order spectra , 2003 .

[5]  Jiying Zhao,et al.  Rotation, Scaling, Translation Invariant Image Watermarking Based on Radon Transform , 2004, CRV.

[6]  Ahmed H. Tewfik,et al.  Geometric Invariance in image watermarking , 2004, IEEE Transactions on Image Processing.

[7]  Chee-Way Chong,et al.  A comparative analysis of algorithms for fast computation of Zernike moments , 2003, Pattern Recognit..

[8]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[9]  Miroslaw Pawlak,et al.  On the Accuracy of Zernike Moments for Image Analysis , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Jia-Guu Leu,et al.  Shape normalization through compacting , 1989, Pattern Recognit. Lett..

[11]  Miroslaw Pawlak,et al.  A multibit geometrically robust image watermark based on Zernike moments , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[12]  Baolong Guo,et al.  Image adaptive RST invariant watermark using pseudo-Zernike moments , 2008 .

[13]  Heung-Kyu Lee,et al.  Invariant image watermark using Zernike moments , 2003, IEEE Trans. Circuits Syst. Video Technol..

[14]  Khalid M. Hosny,et al.  Fast computation of orthogonal Fourier–Mellin moments in polar coordinates , 2009, Journal of Real-Time Image Processing.

[15]  Ahmed H. Tewfik,et al.  Image watermarking by moment invariants , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[16]  Lian Cai,et al.  Rotation, scale and translation invariant image watermarking using Radon transform and Fourier transform , 2004, Proceedings of the IEEE 6th Circuits and Systems Symposium on Emerging Technologies: Frontiers of Mobile and Wireless Communication (IEEE Cat. No.04EX710).

[17]  Zhang Li,et al.  Geometric moment in image watermarking , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..

[18]  M. Pawlak,et al.  A multibit geometrically robust image watermark based on Zernike moments , 2004, ICPR 2004.

[19]  Phen-Lan Lin.  Digital watermarking models for resolving rightful ownership and authenticating legitimate customer , 2001, J. Syst. Softw..

[20]  Frank Hartung,et al.  Multimedia watermarking techniques , 1999, Proc. IEEE.

[21]  M. Teague.  Image analysis via the general theory of moments , 1980 .