可有效抵抗一般性几何攻击的数字水印检测方法

以回归型支持向量机理论为基础, 结合性能稳定的伪Zernike矩和Krawtchouk矩, 提出了一种可有效抵抗一般性几何攻击的强鲁棒数字图像水印检测算法. 该算法首先选取图像的低阶Krawtchouk矩作为特征向量, 然后利用SVR对几何变换参数进行训练学习并对待检测图像进行数据预测, 最后对其进行几何校正并提取水印信息. 仿真实验结果表明, 该数字图像水印检测算法不仅具有较好的不可感知性, 而且对常规信号处理和一般性几何攻击均具有较好的鲁棒性.

An SVR Based Image Watermarking Detection Algorithm against Geometric Attacks

According to the support vector regression (SVR), a new image watermarking detection algorithm against geometric attacks is proposed in this paper, in which the steady pseudo-Zernike moments and Krawtchouk moments are utilized. The main steps of watermark detecting procedure include: 1) some low-order Krawtchouk moments are calculated as the eigenvectors; 2) the appropriate kernel function is selected for training, and an SVR training model can be obtained; 3) the actual output is predicted by using the well trained SVR; 4) the digital watermark is extracted from the corrected test image. Experimental results show that the proposed watermarking detection algorithm is not only robust against common signals processing, but also robust against some geometric attacks.