## 4 VRIS Multidimensional Assessment
### Analysis of Concealment  
To better demonstrate the pixel differences between the encrypted image and the cover image, we enhanced the residual images of the encrypted image and the cover image by 5, 10, 15, and 20 times (Figure 7). The specific experimental results are shown in the figure. When the residual images of the encrypted image and the cover image are enhanced by different multiples, the noise points gradually increase and become scattered, making it more difficult to identify valid information, which helps protect the privacy of the carrier image and the secret image and prevents unauthorized access to sensitive information in the image. Furthermore, the scattered noise points can interfere with the vision of unauthorized users, making it difficult for them to distinguish the information in the image, thereby increasing the difficulty of analysis for unauthorized users and ensuring the concealment of the secret image.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**InceptionV3**} & \multicolumn{3}{c}{**ResNet50**} \\ \hline Dataset & 0.03 & 0.05 & 0.07 & 0.03 & 0.05 & 0.07 \\ Tiny-ImageNet & 70.20\% & 70.95\% & 75.05\% & 99.04\% & 99.12\% & 87.8\% \\ Mini-ImageNet & 75.58\% & 79.5\% & 75.20\% & 69.68\% & 99.24\% & 87.64\% \\
256-ObjectCategories & 74.48\% & 75.99\% & 77.87\% & 74.6\% & 84.68\% & 89.84\% \\ LFW & 81.92\% & 85.78\% & 87.18\% & 86.54\% & 87.64\% & 86.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification error rate of VRIS model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Steganalyzer** & **Steganographic Method** & **Accuracy** \\ \hline \multirow{4}{*}{SRM} & VRIS & 42.29\% \\  & ASDL-GAN [29] & 17.40\% \\  & S-UNIWARD [20] & 20.50\% \\  & ISGAN [32] & 14.85\% \\ \hline \multirow{4}{*}{XuNet} & VRIS & 42.29\% \\  & ASDL-GAN [29] & 16.20\% \\ \cline{1-1}  & S-UNIWARD [20] & 20.10\% \\ \cline{1-1}  & ISGAN [32] & 37.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of detection misclassification rates for cryptographic analyzers.  
We compared and analyzed the residual results of the method proposed in the literature [38], the VRIS model, and the ISGAN model (shown in Figure 8). Generally speaking, the color depth of the residual image reflects the degree of pixel difference between encrypted images and cover images: the darker the color, the smaller the pixel difference between the two, the less the image distortion, and the better the visual quality of encrypted images. When the residual multiplier is 1, the method proposed in the literature [38] has been able to vaguely identify the infrared imaging features of the face, while the residual images generated by both the VRIS model and the ISGAN model present better concealment. With the gradual increase of the residual magnification, the difference becomes gradually more obvious, and when the residual magnification is 5 and 10, observing the residual images of the ISGAN model and the literature [38], it can be found that the cover images can be almost completely recognized. While some detailed differences can also be observed in the residual images of the VRIS model, the degree of difference is significantly lower compared to the ISGAN model and the literature [38], and the cover images cannot be recognized. This suggests that the VRIS model is able to maintain the visual quality of the cover images while embedding the secret images compared to the literature [38] and the ISGAN model, resulting in a higher degree of concealment of the secret images.  
Figure 7: Contrast diagram of residual differences between reconstructed secret image and cover image with varying intensities. (**a**) Cover image; (**b**) encrypted image; (**c**) secret image; (**d**) reconstruction image; (**e**) residual \(\times 1\); (**f**) residual \(\times 5\); (**g**) residual \(\times 10\); (**h**) residual \(\times 15\); (**i**) residual \(\times 20\).