## 5 Discussion  
Finding methods to improve the robustness of content-preserving operations for HRRS images is one of the main problems faced by subject-sensitive hashing--especially robustness against JPEG compression. Transformer has achieved excellent results in many tasks and provided new research paths toward solving existing problems of subject-sensitive hashing. However, there have been no Transformer-based integrity authentication techniques, such as perceptual hashing and subject-sensitive hashing. In this paper, we applied Transformer to subject-sensitive hash algorithms for HRRS images and proposed a new subject-sensitive hash algorithm based on our improved Swin-Unet. From the experiments, the following conclusions could be drawn:  
1. Robustness  
Transformer has demonstrated a significant advantage, in that it can improve the robustness of subject-sensitive hash algorithms. The robustness of the two Transformer-based algorithms in this paper--the Swin-Unet-based algorithm and our improved Swin-Unet-based algorithm--performed better than existing algorithms, especially in robustness against JPEG compression. Compared with the Swin-Unet-based algorithm, our algorithm was slightly less robust; this sacrifice had to be made to increase the algorithm's tampering sensitivity. After all, robustness and tampering sensitivity are essentially a pair of contradictory properties. 2. Tampering sensitivity  
Compared to the algorithm based on the original Swin-Unet, our improved Swin-Unet-based algorithm achieved better tampering sensitivity at the expense of a slight decrease in robustness. Experiments showed that the tampering sensitivity of our improved Swin-Unet-based algorithm was second only to the Attention R2U-Net-based algorithm, outperforming other algorithms. The tampering sensitivity of the Attention R2U-Net-based algorithm was the best among the algorithms compared herein, but its robustness was the worst. As such, we could not recommend it for actual application. 3. Security, and Digestibility Analysis  
The security of our algorithm was based on several aspects; the first of these was the difficulty of Transformer interpretability [49]. In fact, the interpretability of Transformers, in addition the interpretability of deep neural network models [50; 51], has always been a difficult problem for the academic community. However, the difficulty of Transformer interpretability can guarantee the unidirectionality of subject-sensitive hashing; that is to say, Transformer interpretability ensures that valid information from the original HRRS image is hard to get from the hash sequence. The second aspect was the encryption algorithm used in the encoding process. The security of the AES algorithm has been widely recognized; thus, it ensured the security of sensitive hash sequences. Third, the mapping mechanism used in the coding process made nonlinear modifications to the values of image features, which further increased the difficulty of obtaining the original HRRS image features from the hash sequence and enhanced the security of the algorithm. Regarding digestibility, since the main difference of each comparison algorithm lay in the deep neural network model and the feature encoding processes were the same, the digestibility values of each algorithm were the same. 4. Computational performance  
Compared to CNN, Transformer has higher computational complexity. Other application areas of Transformer have faced the problems of long inference time and training time. In this experiment, the two Transformer-based algorithms, namely our algorithm and the original Swin-Unet-based algorithm, also had the disadvantage of low computational performance. In fact, the computational performances of these two algorithms were not only inferior to algorithms based on CNN, such as U-net and M-net, they were also inferior to algorithms based on attention mechanisms, such as AAU-net and Attention U-net.