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为进一步提高基于支持向量机(SVM,support vector machine)水印算法的性能,提出了基于支持向量回归(SVR,support vector regression)的Contourlet域盲水印算法。首先对宿主图像进行Contourlet分解,然后利用SVM建立图像尺度内的局部相关性模型,根据模型的预测结果自适应地嵌入水印。实验结果表明,所提出的算法不仅具有较好的不可感知性,而且对叠加噪声、JPEG压缩、锐化、平滑滤波和对比度增强等常规图像信号处理以及旋转、剪切等几何攻击均具有较好的鲁棒性,其性能明显优于基于SVM的空间域和小波域的水印算法。
In order to further improve the performance of watermarking algorithm based on support vector machine (SVM), a Contourlet domain blind watermarking algorithm based on support vector regression (SVR) is proposed. Firstly, the host image is decomposed by Contourlet. Then local correlation model in the image scale is established by using SVM, and the watermark is adaptively embedded according to the prediction result of the model. The experimental results show that the proposed algorithm not only has better imperceptibility, but also has better performance on conventional image signal processing such as additive noise, JPEG compression, sharpening, smoothing filtering and contrast enhancement as well as geometric attacks such as rotation and shearing The performance of which is obviously better than the watermarking algorithm based on SVM in both spatial and wavelet domain.