JOURNAL ARTICLE

Invariant Image Watermarking Using Accurate Zernike Moments

Ismail

Year: 2010 Journal:   Journal of Computer Science Vol: 6 (1)Pages: 52-59   Publisher: Science Publications

Abstract

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.

Keywords:
Zernike polynomials Velocity Moments Digital watermarking Robustness (evolution) Computer science Invariant (physics) Artificial intelligence Watermark Algorithm Computer vision Affine transformation Mathematics Image (mathematics) Optics Geometry Physics

Metrics

33
Cited By
3.52
FWCI (Field Weighted Citation Impact)
22
Refs
0.93
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Steganography and Watermarking Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Chaos-based Image/Signal Encryption
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Digital Media Forensic Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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