## 4 Experiments
### Tampering Sensitivity Testing of Algorithms  
If an algorithm's tampering sensitivity is too weak, the algorithm will have no useful value, even if the robustness of the algorithm is strong. Although tampering sensitivity and robustness are largely a pair of contradictory attributes, a good subject-sensitive hashing algorithm should try to balance both. First, we tested each algorithm's sensitivity to subtle tampering with random locations. To simulate possible tampering in reality as much as possible, we performed position-random tampering with HRRS images in _Datasets\({}_{10,000}\)_, with each tampering area measuring \(8\times 8\) pixels in size. A set of these tampering areas is shown in Figure 7.  
The results of tampering sensitivity testing for random-position tampering areas \(8\times 8\) pixels in size are shown in Table 9. We used the proportion of instances in which tampering was detected to describe tampering sensitivity, as shown in Equation (4). As shown in Table 9, Attention R2U-Net-based algorithms performed best, in terms of tampering sensitivity. However, the experiments in the previous section showed that its robustness was too poor to be recommended for practical use. The Attention ResU-Net-based algorithm had weaker tampering sensitivity. The tampering sensitivity of our algorithm and the algorithms based on MUM-Net, U-net, M-net, Attention U-Net, and AAU-Net were basically the same, all slightly inferior to the Attention R2U-Net-based  
Figure 7: Comparison of images before and after tampering with a random area (8 \(\times\) 8 pixels): (**a**) original HRRS images, (**b**) altered images. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Each Algorithm** & \(T=0.02\) & \(T=0.03\) & \(T=0.05\) & \(T=0.1\) & \(T=0.2\) \\
**Was Based on** & & & & & \\ \hline MUM-Net & 95.02\% & 90.98\% & 82.19\% & 56.24\% & 14.88\% \\ MultiResUnet & 83.95\% & 78.25\% & 62.52\% & 35.01\% & 11.89\% \\ U-net & 96.98\% & 94.94\% & 85.63\% & 61.39\% & 31.08\% \\ M-net & 96.27\% & 94.31\% & 89.15\% & 66.77\% & 33.16\% \\ Attention U-Net & 93.68\% & 91.29\% & 84.42\% & 68.61\% & 36.24\% \\ Attention ResU-Net & 67.94\% & 58.09\% & 40.66\% & 23.15\% & 7.79\% \\ Attention R2U-Net & 98.75\% & 98.07\% & 95.36\% & 80.93\% & 52.84\% \\ AAU-Net & 92.76\% & 90.92\% & 83.04\% & 65.57\% & 29.52\% \\ Swin-Unet & 84.61\% & 76.42\% & 56.01\% & 23.96\% & 6.92\% \\ Improved Swin-Unet & 94.29\% & 90.16\% & 81.73\% & 55.42\% & 19.24\% \\ (Our algorithm) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Tampering sensitivity testing for random \(8\times 8\) pixel area tampering. algorithm. Moreover, our algorithm was stronger than the Swin-Unet-based algorithm, in terms of tampering sensitivity. To further test each algorithm's tampering sensitivity against random-position tampering, we increase the tampering area to 16 \(\times\) 16 pixels. A set of these tampering areas is shown in Figure 8, in which we selected the same original HRRS images as in Figure 7 to facilitate direct comparison of tampering sensitivity with different granularities. The corresponding tampering sensitivity test results are shown in Table 10.  
As can be seen from Table 10, our algorithm's tampering sensitivity was second only to the Attention R2U-Net-based algorithm, superior to other existing algorithms, and superior to Swin-Unet-based algorithms. Subject-sensitive hashing algorithms are more sensitive to tampering related to a particular subject. Since our algorithm chose buildings as the subject, the algorithms should have had a higher tampering sensitivity to building-related tampering. However, subject-sensitive hashing has not been proposed for a long time, and there was no available public dataset for testing subject-related tampering. To test subject-related tampering sensitivity, we distinguished subject-related tampering into adding buildings and deleting buildings and constructed two datasets for testing these two types of tampering.