# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
## 2 Preliminaries
### Subject-Sensitive Hashing  
Subject-sensitive hashing [9], also known as the subject-sensitive hash algorithm, is a new hashing technology based on perceptual hashing. Subject-sensitive hashing focuses the goal of integrity authentication on the types of features that users are interested in. It overcomes the insufficiency of perceptual hashing and can achieve \"customized\" integrity authentication according to the actual needs of the users. A subject-sensitive hashing algorithm generally includes the following steps: subject-sensitive feature extraction, feature quantization, and feature encoding. In the above steps, subject-sensitive feature extraction focuses on extracting the type of information that users pay attention to, taking into account other types of feature information; feature quantization is conducted to remove the redundancy of the extracted features; and feature encoding is performed to compress and encrypt the quantized features to obtain a subject-sensitive hash sequence. Among them, feature extraction is the key step.  
The existing subject-sensitive hashing algorithms for HRRS images are mainly implemented based on deep learning. In [9], a deep neural network model MUM-Net for subject-sensitive hashing is proposed. The subject-sensitive hash algorithm based on MUM-Net has good robustness and tamper sensitivity, but the model has high computational complexity. In [22], a lightweight deep neural network Semi-U-net is proposed, which overcomes the shortcomings of MUM-Net's low computational efficiency and overly complex model. In [23], a deep neural network model AAU-Net based on the Attention mechanism was proposed, which significantly enhanced the distinction between \"subject-related tampering\" and \"subject-unrelated tampering\".  
Different subject-sensitive hashing algorithms often have significant differences in specific properties. For example, the Attention ResU-Net-based subject-sensitive hashing algorithm has good robustness, but its tamper sensitivity is not good, making it unsuitable for HRRS image integrity authentication [23]. This means that the specific subject-sensitive hashing algorithm implementation process should be determined according to the application requirements of the algorithm.  
However, to the best of our knowledge, there is no research on subject-sensitive hashing algorithms specifically for blockchains. We believe that subject-sensitive hashing algorithms for blockchains should meet the following requirements:  
1. Since the authentication information of HRRS images cannot be modified once it is uploaded to the chain, the subject-sensitive hashing algorithm should be as sensitive as possible to the feature information on multi-level scales to satisfy the later integrity authentication in various environments.
2. Due to the difference in cost sensitivity, undetected tampering often causes more harm to HRRS images. Therefore, the subject-sensitive hashing algorithm used in blockchains should have good tamper sensitivity, and slight tampering of HRRS images should be detected, especially tampering of changing only a few pixels, which is not usually considered tampering in some other applications.
3. The scope of maintaining robustness should not be broad, i.e., it can only maintain robustness for operations that do not affect the later application of HRRS images, such as format conversion and copyright watermark embedding.  
Based on the aforementioned considerations, we designed a blockchain-oriented subject-sensitive hashing algorithm in this paper.