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Proceedings Paper

A zero-watermarking algorithm based on DWT and chaotic modulation
Author(s): Hanqiang Cao; Hua Xiang; Xutao Li; Miao Liu; Sheng Yi; Fang Wei
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Paper Abstract

Digital watermarking is an efficacious technique to protect the copyright and ownership of digital information. But in the traditional methods of watermarking images, the information of original image will be distorted more or less. Facing this problem, a new watermarking approach, zero-watermarking technique, is proposed. The zero-watermarking approach changes the traditional doings that watermarking is embedded into images, and makes the watermarked image distortion-free. Zero-watermarking can successfully solve the conflict between invisibility and robustness. In this paper, a digital image zero-watermarking method based on discrete wavelet transform and chaotic modulation is proposed. The zero-watermarking algorithm based on DWT and chaos modulation consists of watermark embedding and detecting processes. The watermark embedding process is as follow: First, the original image is decomposed to three-level in wavelet domain. Second, some low frequency wavelet coefficients of original image are selected. The selection of the wavelet coefficients is random by chaotic modulation. Third, the character of coefficients selected is used to construct the character watermark. For each coefficient, in comparison with the adjacent coefficient, we can get the character watermark. The watermark extracting process is invert process. The location of the coefficients being extracted is also determined by chaotic sequence. The experimental results show that the watermarking method is invisible and robust against some image processing such as median filtering, JPEG compression, additive Gaussian noise, cropping and rotation attacks and so on. If the initial value of chaos is unknown, the character watermarking can't be extracted correctly.

Paper Details

Date Published: 17 April 2006
PDF: 9 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 624716 (17 April 2006); doi: 10.1117/12.663927
Show Author Affiliations
Hanqiang Cao, Huazhong Univ. of Science and Technology (China)
Hua Xiang, Huazhong Univ. of Science and Technology (China)
Xutao Li, Huazhong Univ. of Science and Technology (China)
Miao Liu, Huazhong Univ. of Science and Technology (China)
Sheng Yi, Huazhong Univ. of Science and Technology (China)
Fang Wei, Huazhong Univ. of Science and Technology (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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