# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 5 Experiment Results and Analysis
### Experiment
#### 5.3.4 Robustness Experiment  
Robustness is a critical property that measures a model's ability to maintain the integrity of both the unique identifiers and the embedded copyright information under intentional or accidental tampering. To comprehensively evaluate the robustness of the proposed BCPM-UI model, extensive experiments were conducted across six diverse vector map datasets: the Shanghai dataset, Beijing dataset, Chengdu dataset, Jiangsu dataset, Hangzhou dataset, and Nanjing dataset. These datasets encompass varying geographic features and complexities, providing a rigorous validation of the model's robustness. The experiments systematically assessed the model's performance under various attack scenarios, including geometric transformations [46], object addition or deletion, layer modification, cropping and merging, format conversion, and reordering attacks. Both the robustness of the unique identifiers and the accurate extraction of copyright information were evaluated to ensure comprehensive protection. To test robustness against geometric transformations, experiments were designed to evaluate the effects of rotation, scaling, and translation (RST). These transformations are common in GIS applications, such as coordinate system adjustments or data visualization. The rotation experiment applied increments of 30\({}^{\circ}\), ranging from 30\({}^{\circ}\) to 150\({}^{\circ}\). Translation experiments shifted data in 100 m intervals up to 300 m, while scaling experiments adjusted data from 60% to 140% of the original size in 20% increments. The results, presented in Figure 11a-c, demonstrate that the BCPM-UI model achieved a bit error rate (BER) of 0 and a normalized correlation (NC) value of 1 across all transformations. These findings confirm that the model is invariant to geometric manipulation, ensuring robustness in practical GIS scenarios that involve frequent RST operations. Object addition or deletion simulates the dynamic evolution of geographic data, where features such as points, lines, and polygons may be modified [47]. To emulate real-world changes, the experiments incrementally added or deleted 10% to 50% of objects in the map. Figure 11d,e and Table 5 show that as the proportion of objects added or deleted increased, the BER rose across all models. However, the BCPM-UI model exhibited the slowest increase in BER. This was attributable to its use of geometric feature parameters, which quantify angular relationships among spatial features, and topological feature parameters, which calculate overall correlations. These features remain largely unaffected by object-level modifications, ensuring resilience against such tampering. Layer attacks, which involve adding or removing one to five layers, assess the model's sensitivity to structural changes in GIS data. These modifications are common in GIS workflows and involve events such as merging datasets or updating map layers. Figure 11f,g and Table 5 reveal that while the BER increased with the addition or removal of more layers, the BCPM-UI model demonstrated the most gradual increase. Even after the removal of five layers, the BCPM-UI model maintained a BER below 0.1, meeting robustness requirements for unique identification. This resilience stems from the geometric and topological parame