基于Blob-Harris特征区域和NSCT-Zernike的鲁棒水印算法

为了有效抵抗水印图像的几何攻击，该文提出了一种基于Blob-Harris特征区域和非下采样轮廓波变换(NSCT)和伪Zernike矩的鲁棒水印算法。首先原始图像进行两层非下采样Contourlet变换后提取其低频图像，然后利用Blob-Harris检测算子对低频图像进行特征点提取，根据各个特征点的特征尺度确定其特征区域，优化筛选出稳定且互不重叠的特征区域并将其四周补零，得到稳定的互不重叠的方形特征区域作为水印嵌入区域，最后计算每一个方形特征区域的Zernike矩，将水印信息嵌入在量化调制正则化Zernike矩的幅值当中。实验结果表明，Lena图峰值信噪比达到40 dB以上时，本文算法对常规图像处理以及缩放、旋转、剪切等几何攻击和组合攻击都有相对较强的鲁棒性。

A Robust Watermarking Algorithm Based on Blob-Harris and NSCT-Zernike

To resist the geometric attack of watermarked images effectively, a robust watermarking algorithm based on Blob-Harris feature region combined with NonSubsampled Contourlet Transform (NSCT) and pseudo Zernike moment is proposed. First, the original image is extracted from its low-frequency image after two-layer NSCT. Then, Blob-Harris detection operator is used to extract the feature points of the low-frequency image. The feature regions are determined according to the feature scale of each feature point, and the stable non-overlapping feature areas are optimized and filtered out and zero padding around them to obtain square feature areas as watermark embedding areas. Finally, the Zernike moments of each square feature area are calculated, the watermarking information is embeded to quantized modulation regularized Zernike moments. The experimental results show that when the peak signal-to-noise ratio of the Lena reaches more than 40 dB, the algorithm has relatively strong robustness to conventional image processing, geometric attacks such as scaling, rotation, and shearing and combined attacks.