Vector data watermark anti-counterfeiting method based on geographical semantics support

Abstract

The invention discloses a vector data watermark anti-counterfeiting method based on geographical semantics support. The method comprises the steps as follows: creating a fidelity description knowledge rule base of vector geographical data based on geographical semantics, creating a watermark carrier model according to the fidelity description knowledge rule base, embedding watermark information based on a typical watermark algorithm, extracting the watermark information, and checking the watermark information. According to the method, the watermark information is embedded into an application-sensitive data characteristic set based on the fidelity description knowledge rule base of the geographical data, so that the watermark robustness is effectively improved; by the typical watermark algorithm, a watermark embedding model taking high fidelity as a solving target guarantees the usability and the invisibility of watermark version data and improves the practicality of the algorithm; through dimensionless treatment of a watermark carrier, a geometric attack is invalid for the watermark model; and a method for adopting repeated embedding in a watermark embedding process and electing in a detecting process effectively improves the resistance of the watermark model to a common watermark attack.

Description

technical field [0001] The invention relates to an anti-counterfeiting method for vector geographic data watermarking, more specifically, an anti-counterfeiting method for vector geographic data watermarking based on geographical semantic support. Background technique [0002] The application of geographic information system (GIS) has been developed to the level of popular social application, which requires the distribution range of spatial data carrying geographic information to be correspondingly wide. In this context, it is imminent to solve the copyright protection problem of spatial data and ensure the safe distribution of geospatial data. As the preferred solution for copyright protection, digital watermarking of geospatial data has aroused the interest of many scholars at home and abroad, and a lot of work has been done on it. [0003] The existing research work can be roughly divided into the following categories: (1) Arrange the data vertices in the local range int...

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(3) According to the relatively stable statistical characteristics of the data set, the statistical variables of the data set are a good watermark carrier, but the data set requires a certain amount of data. If the data is too small, the watermark may not be embedded

[0004] The above model has greatly enriched the theoretical and methodological content of vector space data digital watermarking research, but there are still some problems: (1) The consideration of data fidelity is limited to the absolute error of data fixed points, although there are review literatures that mention shape deformation and spatial relationship deformation, but only a few literatures have considered the control of shape deformation and spatial relationship deformation in the watermark model (2) When recalculating fixed-point coordinates from the watermark carrier, the existing work abstracts the vertex into a pure geometric point. processing, without considering the relationship between the point and neighboring points, let alone the characteristics of the geographic entity where the point is located (3) The first four types of watermark models do not consider the characteristics of the vector data itself, and the established watermark carrier lacks graphic meaning

(4) The existing vector space data watermarking model does not consider the influence of the geographical semantic information of the data and the application background on the fidelity, which is likely to reduce the fidelity, destroy the invisibility of the watermark and the availability of data

Without the support of geographic semantics, the control of fidelity during watermark embedding will become blind and aimless

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