## 4 Experiments
### Robustness Testing of the Algorithms  
The robustness of the algorithm required significant example testing to give stronger confidence to the test results. We used the dataset \(\textit{Datasets}_{10,000}\) in [26] to test each algorithm's robustness. \(\textit{Datasets}_{10,000}\) contained 10,000 test HRRS images cropped from HRRS images from GF-2 satellite, DOTA [48], WHU dataset [47], and other datasets. Each HRRS image in \(\textit{Datasets}_{10,000}\) was in TIFF format and sized at 256 \(\times\) 256 pixels. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Figure 5b** & **Figure 5c** & **Figure 5d** & **Figure 5e** & **Figure 5f** & **Figure 5g** & **Figure 5h** \\ \cline{2-8}
**The Model That** & **Formal** & **Watemark** & **Embedding** & **JPEG** & **Subject-Unrelated** & **Subject-Related** & **8 \(\times\) 8 Random** & **16 \(\times\) 16** \\
**Based on** & **Conversion** & **Embedding** & **Compression** & **Tampering** & **Tampering** & **Tampering** & **Tampering** \\ \hline MUM-Net & Not tampered & Not tampered & Tampered & Tampered & Tampered & Tampered \\ MultiResUnet & Not tampered & Not tampered & Not tampered & Tampered & Tampered & Tampered \\ M-net & Not tampered & Not tampered & Tampered & Tampered & Tampered & Tampered \\ Attention U-Net & Not tampered & Not tampered & Not tampered & Tampered & Tampered & Tampered \\ Attention & Not tampered & Not tampered & Not tampered & Tampered & Tampered & Tampered \\ ResU-Net & Not tampered & Not tampered & Not tampered & Tampered & Tampered & Tampered \\ Attention R2U-Net & Not tampered & Not tampered & Not tampered & Tampered & Tampered & Tampered \\ AAU-Net & Not tampered & Not tampered & Not tampered & Not tampered & Tampered & Tampered \\ Swin-Unet & Not tampered & Not tampered & Tampered & Tampered & Tampered & Tampered \\ (Our algorithm) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Integrity authentication results based on Table 2 (\(T\) = 0.05). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Figure 5b** & **Figure 5c** & **Figure 5d** & **Figure 5e** & **Figure 5f** & **Figure 5g** & **Figure 5h** \\ \cline{2-8}
**The Model That** & **Formal** & **Watemark** & **JPEG** & **Subject-Unrelated** & **Subject-Related** & **8 \(\times\) 8 Random** & **16 \(\times\) 16** \\
**Based on** & **Conversion** & **Embedding** & **Compression** & **Unrelated** & **Related** & **8 \(\times\) 8 Random** & **16 \(\times\) 16** \\
**MUM-Net** & Not tampered & Not tampered & Tampered & Tampered & Tampered & Tampered \\ MultiResUnet & Not tampered & Not tampered & Tampered & Tampered & Tampered & Tampered \\ U-net & Not tampered & Not tampered & Tampered & Tampered & Tampered & Tampered \\ M-net & Not tampered & Not tampered & Tampered & Tampered & Tampered & Tampered \\ M-net & Not tampered & Not tampered & Tampered & Tampered & Tampered & Tampered \\ Improved & & & & & & & \\ Swin-Unet & Not tampered & Not tampered & Tampered & Tampered & Tampered & Tampered \\ Swin-Unet & Not tampered & Not tampered & Tampered & Tampered & Tampered & Tampered \\ (Our algorithm) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Integrity authentication results based on Table 2 (\(T\) = 0.01). First, we tested each algorithm's robustness against JPEG compression. We used C++ as a programming language and used OpenCV2.4.13 interface to implement JPEG compression on the HRRS images of \(\textit{Datasets}_{10,000}\), where the level of JPEG compression was 95%. We used the proportion of images that maintained robustness at a specific threshold \(T\), as shown in Equation (3) above, to describe the robustness of the algorithms.