Volume 5 Number 4 (Apr. 2010)
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JSW 2010 Vol.5(4): 413-420 ISSN: 1796-217X
doi: 10.4304/jsw.5.4.413-420

Research of Blind Watermark Detection Algorithm Based on Generalized Gaussian Distribution

Xiang-wei Zhu
Suzhou Campus, Nanjing Institute of Railway Technology, Suzhou, China

Abstract—In digital management, multimedia content and data can easily be used in an illegal way-- being copied, modified and distributed again. Copyright protection, intellectual and material rights protection for authors, owners, buyers, distributors and the authenticity of content are crucial factors in solving an urgent and real problem. In such scenario, digital watermark techniques are emerging as a valid solution. Blind watermark detection is a modern digital watermark technology with outstanding feature. A novel algorithm of Blind Watermark Detection based on Generalized Gaussian Distribution is proposed in this paper. To start with, this paper carries on the statistical analysis to the high frequency sub-band coefficients of wavelet and contourlet transform, then knowing the high frequency subband coefficients of wavelet and contourlet transform can be characterized by Generalized Gaussian Distribution. So a blind watermark detection algorithm can be designed according to the method of maximum likelihood estimator. Experimental results demonstrate that the performance of watermark detector is good based on Generalized Gaussian Distribution. The scheme is robust against most attack, so it is very effective and practical.

Index Terms—Wavelet Transform, Contourlet Transform, Generalized Gaussian Distribution, Maximum Likelihood Estimator, Blind Watermark Detection.

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Cite: Xiang-wei Zhu, "Research of Blind Watermark Detection Algorithm Based on Generalized Gaussian Distribution," Journal of Software vol. 5, no. 4, pp. 413-420, 2010.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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