## 4 Experiments
### Robustness Testing of the Algorithms
Moreover, as the setting of threshold \(T\) is often not fixed in actual integrity authentication, depending on the specific application and the strength of integrity authentication, we tested each algorithm's robustness under different thresholds; the results are shown in Table 6.  
As shown in Table 6, Transformers have a strong advantage in improving the robustness of subject-sensitive hashing: the Swin-Unet-based algorithm and our improved Swin-Unet-based algorithm both greatly improved the robustness of JPEG compression compared to existing subject-sensitive hash algorithms. With the threshold set to 0.05 or greater, both of these Transformer-based algorithms were 100% robust against JPEG compression. Even at low thresholds (such as 0.02), the robustness against JPEG compression was higher than 95%, reaching a level that existing subject-sensitive hashing algorithms have been unable to achieve. Next, we tested the algorithm's robustness against digital watermarks. Digital watermarking information can be embedded in one band of HRRS images, or embedded in each band. Since multiband watermark embedding makes greater changes to the data, we focused on testing algorithm's robustness against multiband watermarks, as shown in Table 7.  
As can be seen from Table 7, the Swin-Unet-based algorithm performed best against multi-band watermark embeddings, while our improved Swin-Unet-based algorithm was second to it, and superior to the other existing algorithms. \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 & 79.57\% & 90.92\% & 97.61\% & 99.88\% & 100\% \\ MultiResUnet & 76.26\% & 85.95\% & 94.73\% & 98.11\% & 99.59\% \\ U-net & 63.28\% & 75.06\% & 88.85\% & 96.74\% & 99.81\% \\ M-net & 70.22\% & 80.08\% & 92.23\% & 97.58\% & 99.66\% \\ Attention U-Net & 85.94\% & 92.59\% & 97.14\% & 98.88\% & 100\% \\ Attention ResU-Net & 86.02\% & 91.08\% & 96.07\% & 99.26\% & 99.87\% \\ Attention R2U-Net & 58.53\% & 69.22\% & 82.97\% & 95.52\% & 99.26\% \\ AAU-Net & 73.57\% & 81.01\% & 93.33\% & 98.76\% & 99.82\% \\ Swin-Unet & 99.23\% & 99.85\% & 99.97\% & 100\% & 100\% \\ Improved Swin-Unet & & & & & \\ (our algorithm) & 95.88\% & 99.02\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Each algorithm’s robustness against JPEG compression under different thresholds. \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 & 62.09\% & 74.27\% & 88.18\% & 97.32\% & 99.69\% \\ MultiResUnet & 87.48\% & 92.12\% & 96.84\% & 99.06\% & 99.85\% \\ U-net & 61.66\% & 81.75\% & 85.59\% & 95.84\% & 99.28\% \\ M-net & 68.67\% & 88.49\% & 89.97\% & 97.45\% & 99.59\% \\ Attention U-Net & 90.07\% & 94.16\% & 97.25\% & 98.99\% & 99.87\% \\ Attention ResU-Net & 93.58\% & 96.06\% & 98.02\% & 99.77\% & 99.98\% \\ Attention R2U-Net & 48.69\% & 60.24\% & 77.45\% & 91.98\% & 98.51\% \\ AAU-Net & 95.09\% & 97.07\% & 98.33\% & 99.58\% & 99.85\% \\ Swin-Unet & 99.66\% & 99.95\% & 100\% & 100\% & 100\% \\ Improved Swin-Unet & & & & & \\ (Our algorithm) & 98.07\% & 99.74\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Each algorithm’s robustness against watermark embedding. Then, we tested each algorithm's robustness against modification of a small number of pixels. We randomly selected 4 pixels of each HRRS image in \(\textit{Dataset}_{10,000}\) and set them to 0. Figure 6 shows a comparison of a set of images before and after modification. Obviously, the differences between the modified and original images were very small; unless the images were to be enlarged and carefully observed, the differences could not be found.