# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
## 4 Experiments and Discussion
### Performance Analysis
#### 4.3.2 Tamper Sensitivity Test  
Better tamper sensitivity is an important feature of blockchain-oriented subject-sensitive hashing algorithms, especially for detecting subtle tampering in HRRS images. First, we tested the tampering sensitivity of each algorithm based on _Datasets\({}_{10,000}\)_: each HRRS image was tampered at random positions, and the size of the tampered area was 16 \(\times\) 16 pixels, which can simulate subtle tampering of the HRRS image as much as possible. We used the ratio of tampering detected by each algorithm under different thresholds to represent the tampering sensitivity, and the results are shown in Table 7. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \(T\) = 0.02 & \(T\) = 0.03 & \(T\) = 0.05 & \(T\) = 0.10 & \(T\) = 0.20 \\ \hline MD5-based method & 0\% & 0\% & 0\% & 0\% & 0\% \\ SHA-1-based method & 0\% & 0\% & 0\% & 0\% & 0\% \\ \hline RCF-based method & 99.9\% & 100\% & 100\% & 100\% & 100\% \\ MultiResU-Net-based method & 100\% & 100\% & 100\% & 100\% & 100\% \\ Attention U-Net-based method & 99.8\% & 99.8\% & 99.8\% & 99.9\% & 100\% \\ AAU-Net-based method & 99.8\% & 100\% & 100\% & 100\% & 100\% \\ MultiRes-RCF-based method & 99.9\% & 100\% & 100\% & 100\% & 100\% \\ \hline \end{tabular}
\end{table} 
Table 6: Robustness test comparison of watermark embedding (32 bits embedded in single band). \begin{table}
\begin{tabular}{c c c c c} \hline  & \(T\) = 0.02 & \(T\) = 0.03 & \(T\) = 0.05 & \(T\) = 0.10 & \(T\) = 0.20 \\ \hline MD5-based method & 0\% & 0\% & 0\% & 0\% & 0\% \\ SHA-1-based method & 0\% & 0\% & 0\% & 0\% & 0\% \\ \hline RCF-based method & 100\% & 100\% & 100\% & 100\% & 100\% \\ MultiResU-Net-based method & 100\% & 100\% & 100\% & 100\% & 100\% \\ AAU-Net-based method & 100\% & 100\% & 100\% & 100\% & 100\% \\ MultiRes-RCF-based method & 100\% & 100\% & 100\% & 100\% & 100\% \\ \hline \end{tabular}
\end{table} 
Table 5: Robustness test comparison of format conversion (TIFF format to PNG format, no compression). It can be seen from Table 7 that although the tamper sensitivity of each subject-sensitive hash algorithm could be compared with the cryptographic hash algorithms, the tamper sensitivity of the subject-sensitive hashing algorithm based on MultiRes-RCF was better than the other subject-sensitive hash algorithms. Next, we tested the tamper sensitivity of each algorithm to pixel-level modifications. Additionally, based on the _Datasets\({}_{10,000}\)_ dataset, we constructed a tampered dataset: we randomly selected 16-pixel positions of the HRRS image and set the pixels of the three bands in this position to 0. This kind of subtle tampering is not easy to be detected by the human eye, but it can change the feature information carried by HRRS images. The ratio of the tampered images detected is also used to describe the tamper sensitivity of the algorithm, and the results are shown in Table 8:  
It can be seen from Table 8 that the tamper sensitivity of the subject-sensitive hashing algorithm based on the MultiRes-RCF we proposed is still the best, and it can detect these pixel-level tampering well. Of course, there is still a gap compared with the cryptographic hash algorithms. Subject-sensitive hashing is a special case of perceptual hashing, distinguished by its sensitivity to a particular subject. We adopted the dataset _Datasets\({}_{400}\)_ in [23] to test the tamper sensitivity of each algorithm to subject-unrelated tampering. This dataset contains 400 instances of tampering. Since _Datasets\({}_{400}\)_ takes buildings as the subject, the tampering in this dataset does not affect the building information but tampers with trees, grasses, and lakes. The tamper sensitivity test results on _Datasets\({}_{400}\)_ are shown in Table 9: