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|Title:||Maximum-likelihood detection in DWT domain image watermarking using Laplacian modeling||Authors:||Ng, T.M.
|Keywords:||Discrete wavelet transform
Maximum-likelihood (ML) detection
|Issue Date:||Apr-2005||Citation:||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|
|Appears in Collections:||Staff Publications|
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