Paper
20 June 2003 Host-aware spread spectrum watermark embedding techniques
Author Affiliations +
Proceedings Volume 5020, Security and Watermarking of Multimedia Contents V; (2003) https://doi.org/10.1117/12.477326
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States
Abstract
This paper explores techniques that involve the use of the embedder's knowledge of the cover work to help determine the watermark signal to be added to it. While the receiver always seeks to maximize a detection statistic which is a function of an apriori known pseudorandom sequence, the signal added to the cover work by the embedder is allowed to vary, on a per-chip basis, based upon the characteristics of the cover work. Although adaptation of an added watermark signal can be aimed at minimization of visual artifacts, this paper focuses on adaptation of the watermark signal to improve the readability of the signal outside of any human visual system constraints. This idea can be applied in various scenarios. Two specific examples are discussed. When source models are available and maximum-likelihood detection is used, the added watermark signal can be allowed to adapt to host signal variations in order to maximize the likelihood ratio detection statistic used at the receiver. Another instance where per-chip variation can be put to use is when a pre-filter is used to suppress the cover work prior to reading the watermark signal. In this case, the watermark signal is varied in such a way as to maximize the signal at the output of the pre-filter.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hugh Brunk "Host-aware spread spectrum watermark embedding techniques", Proc. SPIE 5020, Security and Watermarking of Multimedia Contents V, (20 June 2003); https://doi.org/10.1117/12.477326
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CITATIONS
Cited by 5 scholarly publications and 2 patents.
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KEYWORDS
Digital watermarking

Sensors

Signal detection

Complex systems

Nonlinear filtering

Discrete wavelet transforms

Image filtering

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