# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 3 The Proposed Method
### Encoder Architecture
#### 3.1.1 Enhanced Images  
Recent research by [PERSON] et al. [33] has demonstrated that edge-guided attention mechanisms can effectively preserve structural details and enhance edge features in images, providing a high-quality, texture-rich medium for secure steganographic embedding. Since edges and texture regions are information-rich while having minimal impact on human visual perception, they are ideal areas for information hiding [34]. Embedding information in these regions minimizes the effect on visual quality while improving the concealment of steganography. Changes in edges and textures help to mask distortions caused by embedding, making steganalysis significantly more difficult. To better learn texture and structural features while preserving image quality, thereby enhancing the security and robustness of steganography, we introduce an additional input stream focused on image enhancement. This stream is designed to amplify texture and structural details, especially edge features. Common image enhancement operators include the Robert, Sobel, Laplacian, and Prewitt operators. In this study, we utilize the Sobel operator for image enhancement. Its convolution kernel is shown in Figure 3. Bidirectional Convolution Kernels. By computing the gradient magnitude, which combines horizontal and vertical gradients, the Sobel operator detects edge information and enhances edge features by adjusting the pixel values of the original image. The corresponding formula is provided below:  
\[C_{enhanced}(x,y)=C(x,y)+\alpha\cdot\parallel\
abla C(x,y)\parallel \tag{1}\]  
\[\
abla C(x,y)=\sqrt{\left(G_{x}*C\right)^{2}+\left(G_{y}*C\right)^{2}} \tag{2}\]  
\(C(x,y)\) represents the pixel value of the original image at the pixel position \((x,y)\). \(G_{x}\) denotes the gradient of the image in the horizontal direction, and \(G_{y}\) denotes the gradient of the image in the vertical direction. \(\alpha\) is the coefficient of the enhancement intensity, and \(*\) denotes the convolution operation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Kernel** & **Input** & **Output** & **Process** \\ \hline Group 1(C) & \(3\times 3\) & 3 & 16 & Convolution—Batch Normalization—Leaky ReLU—MSMA \\ Group 1(E) & \(3\times 3\) & 3 & 16 & Convolution—Batch Normalization—Leaky ReLU \\ Group 2(C) & \(3\times 3\) & 16 & 32 & Convolution—Batch Normalization—Leaky ReLU \\ Group 2(E) & \(3\times 3\) & 16 & 32 & Convolution—Batch Normalization—Leaky ReLU \\ Group 3(C) & \(3\times 3\) & 32 & 64 & Convolution—Batch Normalization—Leaky ReLU \\ Group 4(C) & \(3\times 3\) & 64 & 128 & Convolution—Batch Normalization—Leaky ReLU \\ Group 4(E) & \(3\times 3\) & 64 & 128 & Convolution—Batch Normalization—Leaky ReLU \\ Group 5(C) & \(3\times 3\) & 128 & 128 & Convolution—Batch Normalization—Leaky ReLU \\ InceptionDMK5(E) & \(\backslash\) & 128 & 128 & Split Input—Depthwise Conv \((3\times 3,1\times 11,11\times 1)\)—Concatenate \\ Group 6 & \(5\times 5\) & 128 & 128 & DeConvolution—Batch Normalization—ReLU \\ Skip connection1 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(G_{fusion1}=fusion(IN_{SE}+G_{6})\), \(G_{concat1}=\text{concat}\Big{(}G_{4c},G_{fusion1}\Big{)}\) \\ Group 7 & \(5\times 5\) & 256 & 64 & DeConvolution—Batch Normalization—ReLU \\ Skip connection2 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(G_{fusion2}=fusion(G_{3E}+G_{7})\), \(G_{concat2}=\text{concat}\Big{(}G_{3c},G_{fusion2}\Big{)}\) \\ Group 8 & \(5\times 5\) & 128 & 32 & DeConvolution—Batch Normalization—ReLU \\ Skip connection3 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(G_{fusion3}=fusion(G_{2E}+G_{8})\), \(G_{concat3}=\text{concat}\Big{(}G_{2c},G_{fusion3}\Big{)}\) \\ Group 9 & \(5\times 5\) & 64 & 16 & DeConvolution—Batch Normalization—ReLU \\ Skip connection4 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(G_{fusion4}=fusion(G_{1E}+G_{9})\), \(G_{concat4}=\text{concat}\Big{(}G_{1c},G_{fusion4}\Big{)}\) \\ Group 10 & \(5\times 5\) & 32 & 3 & DeConvolution—Batch Normalization—ReLU \\ \hline \hline \end{tabular}
\end{table}