Paper
5 April 2000 Optimized wavelet domain watermark embedding strategy using linear programming
Shelby Pereira, Sviatoslav V. Voloshynovskiy, Thierry Pun
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Abstract
Invisible Digital watermarks have been proposed as a method for discouraging illicit copying and distribution of copyright material. In recent years it has been recognized that embedding information in a transform domain leads to more robust watermarks. In particular, several approaches based on the wavelet transform have ben proposed to address the problem of image water marking. The advantage of the wavelet transform relative to the DFT or DCT is that it allows for localized water marking of the image. A major difficulty, however, in watermarking in any transform domain lies in the fact that constraints on the allowable distortion at any pixel are specified in the spatial domain. In order to insert an invisible watermark, the current trend has been to model the Human Visual Systems and specify a masking function which yields the allowable distortion for any pixel. This complex function combines contrast, luminance, color, texture and edges. The watermark is then inserted in the transform domain and the inverse transform computed. The watermark is finally adjusted to satisfy the constraints on the pixel distortions. However this method is highly suboptimal since it leads to irreversible losses at the embedding stage because the watermark is being adjusted in the spatial domain with no care for the consequences in the transform domain.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shelby Pereira, Sviatoslav V. Voloshynovskiy, and Thierry Pun "Optimized wavelet domain watermark embedding strategy using linear programming", Proc. SPIE 4056, Wavelet Applications VII, (5 April 2000); https://doi.org/10.1117/12.381690
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CITATIONS
Cited by 25 scholarly publications and 2 patents.
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KEYWORDS
Digital watermarking

Wavelets

Distortion

Image compression

Computer programming

Autoregressive models

Image filtering

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