## 4 Results and Discussion
### Encryption Effect Visualization Analysis  
In this section, the experiment compares the original data, encrypted data, and feature maps to demonstrate the encryption effect of the proposed model. The main purpose of this experiment is to visualize the encryption method's ability to protect the privacy of the original data while still retaining feature information for model use. In the experiment, four random images from the CIFAR-10 dataset were selected as the original data and then processed by the proposed encryption model to generate the encrypted images. Subsequently, through the visualization of the feature maps from the first and second layers, the feature extraction effect of the encrypted images processed by the model is displayed, as shown in Figure 10. This visualization method allows users to observe the changes in the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** \\ \hline Standard Self-Attention [50] & 0.72 & 0.70 & 0.71 & 0.71 \\ Convolutional Block Attention Module [51] & 0.85 & 0.82 & 0.83 & 0.83 \\ Skip Attention & 0.94 & 0.89 & 0.92 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the ablation study on different attention mechanisms.  
original data, encrypted data, and the feature extraction process, facilitating the analysis of the effectiveness of the encryption method.  
As seen in the images, the first row shows the original images clearly presenting different categories from the CIFAR-10 dataset, while the second row displays the encrypted images processed by the proposed model. The encrypted images have significantly lost the visual features of the original images, making it impossible to recognize the specific content of the images with the naked eye, fully demonstrating the effectiveness of the encryption in protecting data privacy. However, in the subsequent feature map visualizations (the third and fourth rows), it can be observed that despite the encrypted input images, the model is still able to extract useful features.  
To scientifically evaluate the effectiveness of the encryption methods, we introduced two commonly used metrics in the experiment: PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index), as shown in Table 7. These metrics were used to compare different encryption methods. PSNR measures the difference between the encrypted and original images, typically calculated by comparing the mean squared error (MSE) between them. A higher PSNR value indicates that the encrypted image is closer to the original image, thus preserving more critical information. The calculation of PSNR involves first determining the MSE between the original and encrypted images, then using this error to compute the PSNR value, which is the logarithmic ratio of the original image's brightness to the MSE. On the other hand, SSIM focuses on structural similarity, which aligns more closely with human visual perception of image quality. SSIM evaluates the similarity between the encrypted and original images by comparing brightness, contrast, and structural information. An SSIM value closer to 1 indicates that the encrypted image is structurally more similar to the original, suggesting that the method effectively preserves essential image information while protecting privacy. The calculation of SSIM involves dividing the image into windows and assessing the brightness, contrast, and structural differences across these windows, which are then combined to yield the final SSIM score. Through quantitative analysis using PSNR and SSIM, our proposed method achieves a well-balanced trade-off between privacy protection and information retention. The experimental results show that the encryption method presented in this study ensures data security while  
Figure 10: Encryption effect visualization.  
effectively extracting and utilizing critical image features, offering an innovative solution that combines data privacy protection with deep learning model efficacy.