# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
## 4 Experiments and Discussion
### Comparative Example of Integrity Authentication
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **Original TIFF** \\ **Format** \\ \end{tabular} & \begin{tabular}{c} **PNG Format** \\ \end{tabular} & \
\begin{tabular}{c} **Watermark** \\ **Embedding** \\ \end{tabular} &
\begin{tabular}{c} **Tampering** \\ **Example 1** \\ \end{tabular} \\ \hline MD5-based & b74c6656 fb1c4f2a & fe9a41989b747 fe4 & cebef967 ac444 ae9 & 14c10f0849123a & 49961768536 ce0 \\ method & bcb433d05 cb9750 & dbc43c1d42251588 & e74 fd3c4 ffc0 be7 & 5762 cb09b26a9d8 & 69 fd319c854f5a60f \\ \hline SHA-1-based method & cld86711825e67 ca9e3 & csc074e1310c4e20e5b & 53829639858 fd908a5c & dbdd725 fc78b6a0c7 bf2 & 04 efdc5e4310d13c52 aa \\  & za7e4369f16d6e9 ae0 & 55b9 fa022 af5 af6 de8b3 & bbf7c54 ec0 efefa5b77a7 & btf479641 eb01d66d80e1 & b2461496a5b21 ee1 efef \\ \hline RCF-based method & 45 ddd812302d5 cft & 45 ddd812302d5 cft & 45 ddd812302d25 cft & 538 bd5 ce4229 ccfcfc & 5347e10 fc7031414 \\  & c8e927 cb9 ca44c30 & c8e927 cb9 ca44c30 & c8e927 cb9 ca44c30 & 8a4277c95c25 bef & 5468457773a2704 \\ \hline MultiResU-Net- & 9 bbb1e2554c0400 & 9 bbb1e2554c0400 & 98b1e2554c0400 & 98 ab1e3554c04003 & 466 acece454c04540 \\  & 316540d40006 eea9 & 31654d040006 eea9 & 31654d640006 eea9 & c1650d60d7006 eea9 & 4565054755 bc6d65 \\ \hline Attention & 60943 cae91b3a2 & 660943 cae91b3a2 & 660943 cae91b3a2 & 60250 fe29a91b3a2 & 3 fe554095995 baeae \\ U-Net-based method & 4943 ff4b0091c42 & 4943 ff4b0091c42 & 4943 ff4b0091c42 & 5943e42a7c0e129129 & 300094794e8613f1 \\ \hline AAU-Net-based & b3676b79401a4 ff1 & b3676b79401a4 ff1 & b3676b79401a4 ff1 & c5 ccff23 ab050 ff0 & a78a944a560f3906 \\ method & 5 bfa0c18879 bf256 & 5 bfa0c18879 bf256 & 5 bfa0c18879 bf256 & 5 afa4c18879 bf211 & aa40f741a31d3c3c \\ \hline MultiRes-RCF-based & 100a0 af2570b19a8 & 100a0 af2570b19a8 & 100a0 af2570b19a8 & 107a05 ad4337d9a8 & 135f56b2940819a8 \\ method & 04036553b5 fc6729 & 04036553 bf5c6729 & 04036553 bf5c6729 & 4036553b5 fc6729 & 3403641 ea5c7729 & 34c33a5c14 cb2724 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hash sequences obtained by different hash algorithms. Figure 6: Examples of authentication for HRRS image: (**a**) Original TIFF format; (**b**) PNG format (TIFF format to PNG format); (**c**) watermark embedding (32 bits embedded in single band); (**d**) Tampering; Example 1; (**e**) Tampering Example 2. According to the experimental results in Tables 2 and 3, we can obtain the integrity authentication results of each comparison algorithm when the threshold \(T\) is set to 0.02, as shown in Table 4.  
It can be seen from Table 4 that the cryptographic Hash functions represented by MD5 and SHA1 take operations such as format conversion and digital watermarking that do not change the image content as tampering, which is not suitable for the blockchain of HRRS images. The four subject-sensitive hashing algorithms based on different deep neural networks can maintain good robustness to operations that do not change the image content and detect operations that change the image content simultaneously, which overcomes the inadequacy of the cryptographic hash function in the blockchain of HRRS images. The above experimental results show that the improved blockchain based on subject-sensitive hashing is more suitable for the integrity authentication of HRRS images than the traditional blockchain. Next, we will prove that our proposed MultiRes-RCF is more suitable for the blockchain of HRRS images.