摘要
针对现有的矢量地理数据零水印算法难以同时满足点、线、面数据的版权保护问题,该文提出了一种运用奇异值分解的矢量地理数据零水印算法。首先,将矢量地理数据进行均匀分块,并利用归一化消除分块内X和Y坐标值的大小差异;其次,利用奇异值分解对矩阵扰动的稳定性,将归一化后的坐标值进行奇异值分解;最终,通过比较奇异值的大小构造特征矩阵,并与置乱后水印图像进行异或运算生成零水印图像。由于该算法利用坐标值构建特征矩阵,因此适用于点、线、面数据。实验结果表明:该算法对平移、缩放、顶点增删、裁剪、精度约减和格式转换攻击具有良好的鲁棒性,同时满足点、线、面数据的版权保护需求。算法实用性更强。
Aiming at the problem that the existing zero-watermarking algorithm for vector geographic data cannot satisfy the copyright protection problem of all type vector data at the same time,this paper proposes a zero-watermarking algorithm for vector geographic data that using singular value decomposition.Firstly,divide the vector geographic data into uniform blocks,and use normalization to eliminate the size difference of X and Y coordinate values in the blocks;Secondly,using singular value decomposition to stabilize the matrix perturbation,the normalized coordinate values are subjected to singular value decomposition;Finally,the feature matrix is constructed by comparing the size of the singular values,and the zero watermark image is generated by XOR operation with the scrambled watermark image.Since the algorithm uses coordinate values to construct a feature matrix,it is suitable for all type vector data.The experimental results show that the algorithm has good robustness to translation,scaling,vertex addition and deletion,clipping,precision reduction and format conversion attacks,and meets the copyright protection requirements of all type vector geographic data.The algorithm is more practical.
作者
王帅
张黎明
李玉
秦如贞
张启航
WANG Shuai;ZHANG Liming;LI Yu;QIN Ruzhen;ZHANG Qihang(Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China;National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring,Lanzhou 730070,China;Gansu Provincial Engineering Laboratory for National Geographic State Monitoring,Lanzhou 730070,China)
出处
《测绘科学》
CSCD
北大核心
2022年第11期196-203,222,共9页
Science of Surveying and Mapping
基金
国家自然科学基金项目(41761080,42271430)
甘肃高等学校产业支撑引导项目(2019C-04)
兰州市人才创新创业科技计划资助项目(2016-RC-59)
兰州交通大学优秀平台支持项目(201806)
关键词
零水印
奇异值分解
归一化
鲁棒性
zero-watermarking
singular value decomposition
normalization
robustness
