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Vector map zero-watermarking algorithm considering feature set granularity
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AI
DOI：10.1016/j.jisa.2024.103955.png)
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Abstract
En 中文
Current vector map zero-watermarking algorithms that integrate blockchain technology typically focus on a limited subset of feature classes within datasets, resulting in significant energy consumption during copyright registration and hindering the advancement of vector map copyright protection through blockchain and zero watermarking techniques. To address this challenge, this paper presents a novel vector map zero watermarking algorithm that considers feature set granularity (ZW-CFSG). This algorithm effectively utilizes boundary contours and internal features to characterize dataset attributes, subsequently converting these features into zero-watermarks. To evaluate the efficacy of the ZW-CFSG algorithm, a comprehensive vector map copyright protection model is developed, integrating both blockchain and zero-watermarking mechanisms. The zero-watermark is securely registered on the blockchain, with energy consumption metrics employed to assess the algorithm's efficiency. Experimental findings reveal that the adoption of the ZW-CFSG algorithm can significantly reduce energy consumption associated with blockchain-based zero-watermarking, thereby enhancing the efficiency of copyright registration while ensuring compliance with rigorous requirements for copyright uniqueness and resilience.
Keywords:
Vector map
Zero watermarking
Feature set granularity
Blockchain
Copyright protection
Journal
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