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Subject: Computer Science
Title:
a new svm-based image watermarking using gaussian-hermite moments
Author: Xiang-yang Wang ; E-no Miao ; Hong-ying Yang
Keyword: Image watermarking ; geometric attack ; Support vector machine ; Gaussian-Hermite moments ; nonsubsampled contourlet transform
Source: Applied Soft Computing
Issued Date: 2011
Volume: 12, Issue:2, Pages:-
Indexed Type: SCIENCEDIRECT ; EI
Department: aYang are with the School of Computer and Information Technology Liaoning Normal University Dalian 116029 China; bState Key Laboratory of Information Security Institute of Software ChineseAcademy of Sciences Beijing 100190 China; cNetwork and Data Security Key Laboratory of Sichuan Province Chengdu 611731 China
Sponsorship: National Natural Science Foundation of China60773031, 60873222; Open Foundation of State Key Laboratory of Information Security of China04-06-1; Open Foundation of Network and Data Security Key Laboratory of Sichuan Province; Open Foundation of Key Laboratory of Modern Acoustics Nanjing University08-02; Institution of Higher Education of China2008351, L2010230
Abstract: Geometric attack is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust image watermarking scheme against geometric attacks. Based on the Support Vector Machine (SVM) and Gaussian-Hermite Moments (GHMs), we propose a robust image watermarking algorithm in Nonsubsampled Contourlet Transform (NSCT) domain with good visual quality and reasonable resistance toward geometric attacks in this paper. Firstly, the NSCT is performed on original host image, and corresponding low-pass subband is selected for embedding watermark. Then, the selected low-pass subband is divided into small blocks. Finally, the digital watermark is embedded into host image by modulating adaptively the NSCT coefficients in small block. The main steps of digital watermark detecting procedure include: (1) some low-order Gaussian-Hermite moments of training image are computed, which are regarded as the effective feature vectors; (2) the appropriate kernel function is selected for training, and a SVM training model can be obtained; (3) the watermarked image is corrected with the well trained SVM model; (4) the digital watermark is extracted from the corrected watermarked image. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as filtering, noise adding, and JPEG compression etc, but also robust against the geometric attacks.
English Abstract: Geometric attack is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust image watermarking scheme against geometric attacks. Based on the Support Vector Machine (SVM) and Gaussian-Hermite Moments (GHMs), we propose a robust image watermarking algorithm in Nonsubsampled Contourlet Transform (NSCT) domain with good visual quality and reasonable resistance toward geometric attacks in this paper. Firstly, the NSCT is performed on original host image, and corresponding low-pass subband is selected for embedding watermark. Then, the selected low-pass subband is divided into small blocks. Finally, the digital watermark is embedded into host image by modulating adaptively the NSCT coefficients in small block. The main steps of digital watermark detecting procedure include: (1) some low-order Gaussian-Hermite moments of training image are computed, which are regarded as the effective feature vectors; (2) the appropriate kernel function is selected for training, and a SVM training model can be obtained; (3) the watermarked image is corrected with the well trained SVM model; (4) the digital watermark is extracted from the corrected watermarked image. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as filtering, noise adding, and JPEG compression etc, but also robust against the geometric attacks.
Language: 英语
WOS ID: WOS:000298631400029
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16016
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Xiang-yang Wang,E-no Miao,Hong-ying Yang. a new svm-based image watermarking using gaussian-hermite moments[J]. Applied Soft Computing,2011-01-01,12(2):-.
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