• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

An Adaptive Watermark Detection Algorithm for Vector Geographic Data

Vol. 14, No. 1, January 31, 2020
10.3837/tiis.2020.01.018, Download Paper (Free):

Abstract

With the rapid development of computer and communication techniques, copyright protection of vector geographic data has attracted considerable research attention because of the high cost of such data. A novel adaptive watermark detection algorithm is proposed for vector geographic data that can be used to qualitatively analyze the robustness of watermarks against data addition attacks. First, a watermark was embedded into the vertex coordinates based on coordinate mapping and quantization. Second, the adaptive watermark detection model, which is capable of calculating the detection threshold, false positive error (FPE) and false negative error (FNE), was established, and the characteristics of the adaptive watermark detection algorithm were analyzed. Finally, experiments were conducted on several real-world vector maps to show the usability and robustness of the proposed algorithm.


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Cite this article

[IEEE Style]
Y. Wang, C. Yang, N. Ren, C. Zhu, T. Rui and D. Wang, "An Adaptive Watermark Detection Algorithm for Vector Geographic Data," KSII Transactions on Internet and Information Systems, vol. 14, no. 1, pp. 323-343, 2020. DOI: 10.3837/tiis.2020.01.018.

[ACM Style]
Yingying Wang, Chengsong Yang, Na Ren, Changqing Zhu, Ting Rui, and Dong Wang. 2020. An Adaptive Watermark Detection Algorithm for Vector Geographic Data. KSII Transactions on Internet and Information Systems, 14, 1, (2020), 323-343. DOI: 10.3837/tiis.2020.01.018.

[BibTeX Style]
@article{tiis:23235, title="An Adaptive Watermark Detection Algorithm for Vector Geographic Data", author="Yingying Wang and Chengsong Yang and Na Ren and Changqing Zhu and Ting Rui and Dong Wang and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.01.018}, volume={14}, number={1}, year="2020", month={January}, pages={323-343}}