基于支持向量回归的Contourlet域盲水印算法

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。

Blind watermarking scheme in contourlet domain based on support vector regression

To further enhance the performance of existing watermarking schemes based on the support vector machine(SVM),an image watermarking scheme in contourlet domain based on support vector regression(SVR) is proposed in this paper.Firstly,the host image is decomposed by the contourlet transform.Then,the non-linear local correlation model of the image is established using the support vector machine.The watermark is adaptively embedded according to the prediction result of the established model.Experimental results show that the proposed scheme is not only invisible and robust against the common image signal processing,such as noise adding,JPEG compression,sharpening,smoothing filtering and contrast enhancement,but also robust against the geometric attacks,such as rotation,shearing and distortion.Especially,its performance is significantly superior to the image watermarking schemes in spatial domain or wavelet domain based on the support vector machine.