# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
## 4 Experiments and Discussion
### Performance Analysis
#### 4.3.1 Robustness Test  
Robustness is the most obvious difference between subject-sensitive hashing and cryptographic hashing. Unlike applications of deep neural networks such as image recognition and image segmentation, subject-sensitive hashing requires a large amount of data to be tested to make the results more convincing and reliable. We adopted the dataset _Datasets\({}_{10,000}\)_ in [23] to test the robustness of each algorithm. This dataset contains 10,000 HRRS images of 256 \(\times\) 256 pixels stored in TIFF format, consisting of Gaofen-2 (GF-2) satellite, DOTA [32], and some test images from [9,22].  
First, we tested the robustness of each algorithm to format conversion. Here, we converted the images in TIFF format in _Datasets\({}_{10,000}\)_ to PNG format without data compression and then compared the normalized Hamming distance between the hash sequences before and after the format conversion. Under different thresholds \(T\), the robustness test results of each algorithm are shown in Table 5:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **PNG Format** &
\begin{tabular}{c} **Watermark** \\ **Embedding** \\ \end{tabular} & **Tampering Example 1** & **Tampering Example 1** \\ \hline MD5-based method & Tampered with & Tampered with & Tampered with & Tampered with \\ SHA-1-based method & Tampered with & Tampered with & Tampered with & Tampered with \\ \hline RCF-based method & Unchanged & Unchanged & Tampered with & Tampered with \\ MultiResU-Net-based method & Unchanged & Unchanged & Tampered with & Tampered with \\ Attention U-Net-based method & Unchanged & Unchanged & Tampered with & Tampered with \\ AAU-Net-based method & Unchanged & Unchanged & Tampered with & Tampered with \\ MultiRes-RCF-based method & Unchanged & Unchanged & Tampered with & Tampered with \\ \hline \hline \end{tabular}
\end{table}
Table 4: Integrity authentication results after each operation are shown in Figure 6 (threshold \(T\) is set to 0.02).  
It can be seen from Table 5 that due to the image of avalanche effect, the two cryptographic hash functions MD5 and SHA1 were not robust to format conversion, i.e., the HRRS images before and after format conversion were regarded as two different images by MD5 and SHA1, which is obviously not conducive to the integrity authentication of HRRS images. All the subject-sensitive hash algorithms involved in the test had a better robustness-to-format conversion.  
Next, we tested the robustness of each algorithm to digital watermark embedding. In watermark embedding, 24-bit watermark information is embedded in a single band of the image, and the other bands remain unchanged. The watermark information of each bit is embedded in the second most significant bit of the pixel. The robustness test results after watermark embedding are shown in Table 6:  
It can be seen from Table 6 that each subject-sensitive hashing algorithm had better robustness to digital watermark embedding. For example, for the MultiRes-RCF-based method, if the threshold \(T\) is set to 0.02, 99.9% of HRRS images can pass the integrity authentication after watermark embedding, i.e., the normalized Hamming distance of the hash sequence before and after the watermark embedding is lower than 0.02. If the threshold \(T\) is set to a value of 0.03 or greater, all HRRS images can pass the integrity authentication after embedding the watermark.