Lossless Watermarking Algorithm for Geographic Point Cloud Data Based on Vertical Stability

被引:1
|
作者
Zhang, Mingyang [1 ]
Dong, Jian [1 ,2 ]
Ren, Na [3 ,4 ]
Guo, Shuitao [3 ]
机构
[1] Dalian Naval Acad, Dept Mil Oceanog & Hydrog & Cartog, Dalian 116018, Peoples R China
[2] Dalian Naval Acad, Key Lab Hydrog Surveying & Mapping PLA, Dalian 116018, Peoples R China
[3] Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Peoples R China
[4] Third Surveying & Mapping Inst Hunan Prov, Hunan Engn Res Ctr Geog Informat Secur & Applicat, Changsha 410018, Peoples R China
基金
中国国家自然科学基金;
关键词
lossless watermarking; geospatial point cloud data; vertical stability; data storage order; robustness; SCHEME;
D O I
10.3390/ijgi12070294
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the increasing demand for high-precision and difficult-to-obtain geospatial point cloud data copyright protection in military, scientific research, and other fields, research on lossless watermarking is receiving more and more attention. However, most of the current geospatial point cloud data watermarking algorithms embed copyright information by modifying vertex coordinate values, which not only damages the data accuracy and quality but may also cause incalculable losses to data users. To maintain data fidelity and protect its copyright, in this paper, we propose a lossless embedded watermarking algorithm based on vertical stability. First, the watermark information is generated based on the binary encoding of the copyright information and the code of the traceability information. Second, the watermark index is calculated based on the length of the watermark information after compression and the vertical distribution characteristics of the data. Finally, watermark embedding is completed by modifying the relative storage order of the corresponding data according to the index and watermark value. The experimental results show that the proposed algorithm has good invisibility without damaging the data accuracy. In addition, compared with existing algorithms, this method has a higher robustness under operations such as projection transformation, precision perturbation, and vertex deletion of geospatial point cloud data.
引用
收藏
页数:21
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