Vector map zero-watermarking algorithm considering feature set granularity

Abstract

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.