## 3 VRIS Image Steganography Model
### Basic Results of the VRIS Basic Model
#### 3.3.3 Hidden-Insight  
The Hidden-Insight module (as shown in Figure 5 and Algorithm 2) is meticulously designed to extract concealed secret images from second-level encrypted images, addressing the needs of legitimate users with precision. Its design principles primarily leverage convolutional neural network architectures found in deep learning. Deep learning leverages the superior capabilities of this architecture in feature extraction and representation learning. In the architecture of the Hidden-Insight module, the convolutional layers within the module significantly reduce the number of parameters through parameter sharing, effectively lowering the risk of overfitting while capturing the local correlations within images. This aspect is crucial for extracting key features of secret images. In order to fully cope with the multi-scale characteristics that secret image information may present, the Hidden-Insight module sets up convolutional kernels of different sizes and configurations in the first few layers to capture feature information at different scales, and accelerates the training process to enhance the generalization ability of the model so that it can cope with different input data. \begin{table}
\begin{tabular}{c c c c} \hline
**Block** & **Input** & **Structure** & **Output Size** \\ \hline Block1 & (batch\_size, 3, H, W) & Conv Layer + LeakyReLU & (batch\_size, 64, H/2, W/2) \\ Block2 & (batch\_size, 64, H/2, W/2) & Conv Layer + BN + LeakyReLU & (batch\_size, 128, H/4, W/4) \\ Block3 & (batch\_size, 128, H/4, W/4) & Conv Layer + BN + LeakyReLU & (batch\_size, 256, H/8, W/8) \\ Block4 & (batch\_size, 256, H/8, W/8) & Conv Layer + BN + LeakyReLU & (batch\_size, 512, H/16, W/16) \\ Block5 & (batch\_size, 512, H/16, W/16) & Conv Layer + Sigmoid & (batch\_size, 1, 1, 1) \\ \hline \end{tabular}
\end{table} 
Table 2: AI-Evasion Shield network structure details. ```
1:FUNCTIONInitialize_Hidden_Insight(input_S):
2:# Step 1: Perform preliminary feature extraction and transformation on the carrier image
3: x1 = relu(conv1(input_S))# Extract first feature map
4: x2 = relu(conv2(input_S))# Extract second feature map
5: x2 = PAD(x2, (0, 1, 0, 1))# Pad x2 to ensure consistent sizes
6: x3 = relu(conv3(input_S))# Extract third feature map
7: x_new1 = CONCATENATE (x1, x2, x3)# Concatenate feature maps along the channel dimension
8:# Step 2: Further transform the concatenated feature map x_new1
9: x1\({}^{\prime}\) = relu(conv4(x_new1))# Extract new features
10: x2\({}^{\prime}\) = relu(conv5(x_new1))# Extract new features
11: x2\({}^{\prime}\) = PAD (x2, (0, 1, 0, 1))# Pad x2
12: x3\({}^{\prime}\) = relu(conv6(x_new1))# Extract new features
13: x_new2 = CONCATENATE (x1', x2', x3')# Concatenate feature maps
14: x_andas = CONCATENATE (input_S, x_new2)# Concatenate carrier image with x_new2
15:# Step 3: Repeat the process from step 2 three times to deepen the network
16: FOR i FROM 1 TO 2: # Repeat twice
17: x1 = relu(conv7(x_new2))# Extract additional features
18: x2 = relu(conv8(x_new2))# Extract additional features
19: x2 = PAD (x2, (0, 1, 0, 1))# Pad x2
20: x3 = relu(conv9(x_new2))# Extract additional features
21: x4_deep = CONCATENATE (x1, x2, x3)# Update x4_deep
22:# Step 4: Transform the final feature map
23: output_image = tanh(conv16(x4_deep))# Process with conv16 and apply tanh activation
24: RETURN output_image # Secretly Reconstructed Image
```  
**Algorithm 2** Algorithm of the Hidden-Insight. As the second-level encrypted image processes through the layers, Hidden-Insight gradually refines the core features of the secret image through multiple convolutions, activations (such as ReLU), and feature concatenations. Particularly in the final layers, carefully designed combinations of convolutional layers further enrich the representation and accuracy of the features. Lastly, to ensure the visual naturalness and authenticity of the output image, the Hidden-Insight module employs a tanh activation function in the final layer.