周琳1,,,
梁先明2,
徐伟1
1.重庆邮电大学通信与信息工程学院信号与信息处理重庆市重点实验室 重庆 400065
2.中国西南电子技术研究所 成都 610036
基金项目:国家自然科学基金(61701067, 61771085, 61671095, 61702065)；信号与信息处理重庆市市级重点实验室建设项目(CSTC2009CA2003)；重庆市研究生科研创新项目(CYS19248)；重庆市教育委员会科研项目(KJ1600427, KJ1600429)
详细信息
作者简介:张天骐：男，1971年生，博士后，教授，主要研究方向为语音信号处理、通信信号的调制解调、盲处理、神经网络实现以及FPGA，VLSI实现
周琳：女，1995年生，硕士生，研究方向为图像与信号处理、图像水印
梁先明：男，1976年生，工程师，研究方向为通信侦查领域信号处理及信号分析等
徐伟：男，1993年生，硕士生，研究方向为通信信号处理等
通讯作者:周琳 614254097@qq.com
中图分类号:TN911.73计量
文章访问数:409
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被引次数:0
出版历程
收稿日期:2020-03-10
修回日期:2020-11-30
网络出版日期:2020-12-05
刊出日期:2021-07-10
A Robust Watermarking Algorithm Based on Blob-Harris and NSCT-Zernike
Tianqi ZHANG1,Lin ZHOU1,,,
Xianming LIANG2,
Wei XU1
1. School of Communication and Information Engineering, Chongqing Key Laboratory of Signal and Information Processing (CQKLS&IP), Chongqing University of Posts and Telecommunications (CQUPT), Chongqing 400065, China
2. Southwest China Institute of Electronic Technology, Chengdu 610036, China
Funds:The National Natural Science Foundation of China (61701067, 61771085, 61671095, 61702065), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009CA2003), The Chongqing Graduate Research and Innovation Project (CYS19248), The Research Project of Chongqing Educational Commission(KJ1600427, KJ1600429)
摘要
摘要: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。
关键词:水印/
特征区域/
非下采样轮廓波变换/
伪Zernike矩
Abstract: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.
Key words:Watermarking/
Feature region/
NonSubsampled Contourlet Transform(NSCT)/
Pseudo-Zernike moment
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https://jeit.ac.cn/article/exportPdf?id=108be07b-a628-42b6-a031-c7386c1c1f6c
