## 2 Related Work
### Subject-Sensitive Hashing  
Subject-sensitive hashing, also known as subject-sensitive hash algorithm, inherently has the robustness and sensitivity to tampering of perceptual hashing [6]. It can meet customized requirements in specific fields for integrity authentication and perform sensitive integrity authentication for specific subjects (such as buildings or roads). Subject-sensitive hashing is not an upgraded version of perceptual hashing, nor can it replace perceptual hashing. However, it is difficult for traditional image technology to define subject-related features. Subject-sensitive feature extraction is essentially a feature extraction process in which information of specific feature types is weighted. Deep learning has an excellent capacity for feature expression, and can achieve feature-weighted feature extraction by learning specific samples, reducing the complexity of artificially-designed features [19; 20; 21]. Existing subject-sensitive hashing methods mainly use convolutional neural networks (CNNs) to achieve feature extraction of HRRS images. In addition to MUM-net [6], other models, such as U-net [22], Attention U-net [23], M-net [24], and MultiResUNet [25] can also be used to implement subject-sensitive hashing. However, CNNs need to continuously stack convolutional layers to achieve the extraction of image local information to image global information. This causes the model to be bloated, brings about the problem of gradient disappearance, and even leaves the network unable to converge. Moreover, methods based on CNN adopt the method of feature downsampling, which reduces the ability of the algorithm to detect small-scale tampering. In addition, current CNN-based, subject-sensitive hashing algorithms are generally less robust to pixel-level content-preserving operations. The outstanding performance issue is that the robustness of these algorithms against JPEG compression needs to be further optimized. Although the introduction of the attention mechanism enhances the algorithm robustness to a certain extent [26], it still needs further improvement. With the interdisciplinary development of deep learning, Transformer stands out in computer vision (CV) and provides new possibilities for solutions to problems faced by CNN-based subject-sensitive hashing [27]. Since ViT replaced the convolutional structure with the Transformer structure for vision tasks, Transformer has been successfully applied in the fields of image segmentation [28; 29], image classification [30; 31], image super-resolution [32; 33], image restoration [34], and object detection [35; 36]. Transformer-based computer vision methods exploit the self-attention mechanism in Transformer to explore long-range dependencies and learn the attentional interactions of different patch tokens. In VIT, the input image is cropped into fixed-size image patches to transform the image into sequence data that the Transformer structure can process. Then, each image patch is changed into a one-dimensional vector, which is linearly mapped and then added to position encoding. Inspired by ViT, TransUNet [37] adopts a Transformer-based encoder to process image patch sequences and combines the characteristics of U-net. Swin Transformer (Shifted Windows Transformer) [38] uses a hierarchical strategy to restrict the attention computation to a window, aiming to introduce a locality operation, similar to CNN convolution, and significantly reduce the computational cost. Swin-Unet [39] combines the characteristics of Swin Transformer and U-net, and is a pure Transformer-based image segmentation network. Swin-Unet draws on the network structure characteristics of U-net, and sends the tokenized image blocks to U-shaped network architecture composed of pure Transformer through skip connections for local and global feature learning. This method of using small image blocks as the basic processing unit (instead of pixels) brings new possibilities to enhance the robustness of pixel-level operations.