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Proceedings Paper

A novel multi-strategy watermark embedding technique
Author(s): Gui Feng; QiWei Lin
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Paper Abstract

Different digital watermarking schemes had been proposed to address this issue of ownership identification. Early work on digital watermarking focused on information hiding in the spatial domain or transform domain respectively. Usually the watermark recovered result was not as satisfaction as the demand. Some multi-describing techniques were proposed for watermark embedding lately. Enlightened by these techniques, a novel blind digital image watermarking algorithm based on multi strategy is put forward in this paper. The watermark is embedded in multi-resolution wavelet transform domain of the original image. Based on spread spectrum techniques, the algorithm is composed of three new techniques to improve robustness, imperceptibility and security. These new techniques are as follow: First, multi- intensity embedding technique is adopted in the embedding process. Because the intensity of watermark has different influences to wavelet coefficient in different resolution layer, so using different intensity in corresponding layer, we can gain the merit of stronger anti-attack ability and imperceptibility Second, applying spread spectrum code to permute the original watermark so a new scrambling watermark is established. By reducing the effect of destroyed watermark image, the technique has the ability of anti-clipping, moreover, the technique improves the security of watermarking for needing scrawling password to extract the original watermark. Third, interlaced watermark embedding technique is introduced. In this technique, we interlace several copies of watermark in different resolution to embed in wavelet transform domain. As a result the recovered watermark is shown better performance after various attacks.

Paper Details

Date Published: 17 April 2006
PDF: 11 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 624713 (17 April 2006); doi: 10.1117/12.660545
Show Author Affiliations
Gui Feng, HuaQiao Univ. (China)
QiWei Lin, HuaQiao Univ. (China)

Published in SPIE Proceedings Vol. 6247:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Harold H. Szu, Editor(s)

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