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基于支持向量机的小波域自适应水印算法
引用本文:李春花,凌贺飞,卢正鼎.基于支持向量机的小波域自适应水印算法[J].华中科技大学学报(自然科学版),2007,35(10):32-34.
作者姓名:李春花  凌贺飞  卢正鼎
作者单位:华中科技大学,计算机科学与技术学院,湖北,武汉,430074
基金项目:国家自然科学基金 , 湖北省自然科学基金
摘    要:提出一种小波域中基于小波系数方向树模型的支持向量机自适应水印算法.利用支持向量机在小样本的情况下具有良好的学习和泛化性能的特点,建立小波域内不同尺度上同方向子带内相同空间位置的小波系数间的关系模型;根据关系模型来嵌入和提取水印.为提高图像视觉效果,采用模糊聚类的方法自适应地选取水印的嵌入位置.实验结果表明:该方法对常见的图像攻击具有很好的鲁棒性,而且水印的隐蔽性好、安全性强.

关 键 词:数字水印  小波变换  方向树模型  支持向量机  模糊聚类  支持向量机  小波域  自适应  水印算法  support  vector  machine  watermarking  adaptive  安全性  隐蔽性  鲁棒性  图像攻击  结果  实验  嵌入位置  选取  方法  模糊聚类  视觉效果  提取水印  关系模型
文章编号:1671-4512(2007)10-0032-03
修稿时间:2007-04-17

A DWT domain adaptive watermarking using support vector machine
Li Chunhua,Ling Hefei,Lu Zhengding.A DWT domain adaptive watermarking using support vector machine[J].JOURNAL OF HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY.NATURE SCIENCE,2007,35(10):32-34.
Authors:Li Chunhua  Ling Hefei  Lu Zhengding
Institution:College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Abstract:A DWT domain image watermarking scheme based on the direction-tree structure was proposed,and by using support vector machine(SVM) it became possible to detect the inherent relation between wavelet coefficients in the same direction subband and the same spatial location but different resolution.Due to good learning ability and generalizing ability of SVM under the limited training samples,it can well learn the above relation.According to the relation model,a bit of the watermark was embedded and extracted.To be more imperceptible,the author used fuzzy C-mean clustering algorithm to adaptively determine the watermark embedding locations.Moreover,the watermark recovery did require the original image.Experimental results show that the proposed algorithm had good perceptual quality and high robustness to common image processing operation and the JPEG lossy compression.
Keywords:image watermarking  discrete wavelet transform(DWT)  direction-tree-model(DTM)  support vector machine(SVM)  fuzzy clustering
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