A Zero-Watermarking Algorithm for Vector Maps Based on ResNet50 Model
Aiming at the problems of insufficient ability to resist geometric attacks and element addition and deletion attacks in traditional vector map zero watermarking algorithms,this paper proposes a novel zero-watermarking algorithm for vector maps based on convolutional neural networks(CNNs),aiming for lossless copyright protection of vector map data.The algorithm employs the Douglas-Peucker algorithm to extract the feature elements of vector maps,maps coordinate sets into sequences suitable for the ResNet50 model processing,and then automatically extracts features from the vector map data to construct the zero watermark through the ResNet50 model.Selecting the vector map datasets of catering,villages,and township roads in Xi'an as experimental data for algorithm verification,the experimental results shows that the proposed algorithm exhibits excellent performance in resisting both geometric attacks and element addition/deletion attacks,effectively protecting the copyright of vector map data.It also holds broad application potential in lossless copyright protection for vector maps and contributes to safeguarding the commercial interests of data providers.
vector mapzero watermarkResNet50copyright protectiongeographic information security
万方数据
维普
