Please use this identifier to cite or link to this item: https://doi.org/10.1109/LSP.2005.843776
Title: Maximum-likelihood detection in DWT domain image watermarking using Laplacian modeling
Authors: Ng, T.M. 
Garg, H.K. 
Keywords: Discrete wavelet transform
Laplacian
Maximum-likelihood (ML) detection
Neyman-Pearson
Issue Date: Apr-2005
Source: Ng, T.M., Garg, H.K. (2005-04). Maximum-likelihood detection in DWT domain image watermarking using Laplacian modeling. IEEE Signal Processing Letters 12 (4) : 285-288. ScholarBank@NUS Repository. https://doi.org/10.1109/LSP.2005.843776
Abstract: Digital image watermarks can be detected in the transform domain using maximum-likelihood detection, whereby the decision threshold is obtained using the Neyman-Pearson criterion. A probability distribution function is required to correctly model the statistical behavior of the transform coefficients. Earlier work has considered modeling the discrete wavelet transform coefficients using a Gaussian distribution. Here, we introduce a Laplacian model and establish via simulation that it can result in a better performance than the Gaussian model. © 2005 IEEE.
Source Title: IEEE Signal Processing Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/56595
ISSN: 10709908
DOI: 10.1109/LSP.2005.843776
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