Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/12978
Title: Statistical models for digital watermarking
Authors: NG TEK MING
Keywords: Likelihood ratio, Neyman-Pearson criterion, Generalized Gaussian, Generalized gamma, Laplacian, Invisible watermark
Issue Date: 20-Dec-2007
Source: NG TEK MING (2007-12-20). Statistical models for digital watermarking. ScholarBank@NUS Repository.
Abstract: This thesis is directed towards the study of the likelihood ratio (LR) detection method in detecting invisible watermark in images. LR detection method based on Bayes' decision theory has been considered for image watermarking in transform domain. The Neyman-Pearson criterion is used to derive a decision threshold. In order to achieve an optimum behavior of the LR detector, a probability distribution function (PDF) that models the distribution of the transform coefficients is required. We propose using the generalized Gaussian PDF and Laplacian PDF to model transform coefficients of discrete wavelet transform (DWT), and the generalized gamma PDF to model coefficients of discrete Fourier transform (DFT). Decision rule and closed-form decision threshold are derived for all proposed models. New estimators are introduced for parameters of the generalized Gaussian and generalized gamma distributions. Our numerical experiments reveal that the proposed models can produce better LR detection.
URI: http://scholarbank.nus.edu.sg/handle/10635/12978
Appears in Collections:Ph.D Theses (Open)

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