## 4 Experiments
### Tampering Sensitivity Testing of Algorithms
The two datasets each contained 200 sets of tampering instances, named \(Data_{addbuildings}\) and \(Data_{deletebuildings}\). Figures 9 and 10 show tampering examples of adding buildings and deleting buildings, respectively. The tampering sensitivity tests for adding buildings are shown in Table 11. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**The Model That Each** & \multirow{2}{*}{\(T\) = 0.02} & \multirow{2}{*}{\(T\) = 0.03} & \multirow{2}{*}{\(T\) = 0.05} & \multirow{2}{*}{\(T\) = 0.1} & \multirow{2}{*}{\(T\) = 0.2} \\
**Algorithm Based on** & & & & & \\ \hline MUM-Net & 98.79\% & 98.52\% & 95.25\% & 78.71\% & 42.77\% \\ MultiResUnet & 99.05\% & 97.73\% & 92.43\% & 79.26\% & 54.29\% \\ U-net & 98.85\% & 97.98\% & 96.24\% & 86.41\% & 59.17\% \\ M-net & 99.16\% & 99.03\% & 96.22\% & 86.98\% & 60.71\% \\ Attention U-Net & 98.86\% & 97.95\% & 96.16\% & 86.39\% & 59.21\% \\ Attention ResU-Net & 83.92\% & 76.68\% & 57.52\% & 34.74\% & 12.11\% \\ Attention R2U-Net & 99.92\% & 99.90\% & 99.85\% & 98.03\% & 81.54\% \\ AAU-Net & 99.54\% & 99.19\% & 96.43\% & 87.01\% & 64.24\% \\ Swin-Unet & 93.23\% & 89.29\% & 75.22\% & 32.61\% & 7.64\% \\ Improved Swin-Unet & 99.72\% & 99.41\% & 98.35\% & 88.62\% & 38.68\% \\ (Our algorithm) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Tampering sensitivity test for random 16 \(\times\) 16 pixel tampering. Figure 8: Comparison of images before and after being tampered with random area (16 \(\times\) 16 pixel area): (**a**) Original HRRS images (The same as Figure 7a), (**b**) altered images. Figure 10: Examples of subject-related tampering (deleting buildings): (a) original HRRS images, (b) altered images. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Each Algorithm** & \(T\) = 0.02 & \(T\) = 0.03 & \(T\) = 0.05 & \(T\) = 0.1 & \(T\) = 0.2 \\ \hline MUM-Net & 100\% & 100\% & 99.0\% & 98.0\% & 60.5\% \\ MultiResUnet & 96.0\% & 94.5\% & 84.0\% & 56.0\% & 10.5\% \\ U-net & 100\% & 100\% & 99.5\% & 95.0\% & 65.0\% \\ M-net & 100\% & 100\% & 99.5\% & 94.0\% & 58.5\% \\ Attention U-Net & 100\% & 100\% & 98.0\% & 92.5\% & 50.0\% \\ Attention ResU-Net & 75.5\% & 67.0\% & 43.0\% & 19.5\% & 1.5\% \\ Attention R2U-Net & 100\% & 100\% & 100\% & 98.0\% & 77.0\% \\ AAU-Net & 100\% & 100\% & 98.0\% & 89.5\% & 36.0\% \\ Swin-Unet & 97.5\% & 90.0\% & 67.5\% & 20.0\% & 3.5\% \\ Improved Swin-Unet & 100\% & 100\% & 99.5\% & 96.0\% & 41.5\% \\ (Our algorithm) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Tampering sensitivity tests for subject-related tampering (adding buildings). Figure 9: Examples of subject-related tampering (adding buildings): (a) original HRRS images, (b) altered images. Figure 10: Examples of subject-related tampering (deleting buildings): (a) original HRRS images, (b) altered images. As can be seen from Table 11, each algorithm demonstrated good sensitivity to tampering by adding buildings. Each algorithm's tampering sensitivity test for subject-related tampering (adding buildings) was ideal, and our algorithm was second only to the Attention R2U-Net-based algorithm at a low threshold (less than or equal to 0.05). The tampering sensitivity tests for deleting buildings are shown in Table 12. Although the algorithms' sensitivity to tampering by deleting buildings did not significantly vary, our algorithm performed best at this test. Comprehensively analyzing and summarizing the data shown Tables 9-12, we concluded that, among the compared algorithms, the Attention R2U-Net-based algorithm's tampering sensitivity was the best. However, its robustness was the worst, as stated in the conclusion of the previous section, meaning that the comprehensive performance of the algorithm was not good. Our algorithm's tampering sensitivity was second only to that of the Attention R2U-Net-based algorithm. Thus, our improved Swin-Unet compensated for the original Swin-Unet's lack of tampering sensitivity.