# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 4 Experiments
### Ablation Study  
To comprehensively evaluate the effectiveness of our proposed edge-enhanced dual-stream U-Net architecture, multi-scale median attention (MSMA) module, and InceptionDWK module in steganographic models, we conducted a systematic ablation study on the Div2k dataset. The primary goal of this study is to assess the impact of these modules on model performance. By progressively removing each module and observing the resulting performance changes, we quantitatively analyze the contribution of each module.  
In the ablation experiments on the edge-enhanced dual-stream U-Net structure (EDSU), we replace the dual-stream structure with the original U-Net structure with the edge-enhanced input stream removed in order to analyze the contribution of the edge-enhancement feature to the quality of the steganographic image and the decoding performance. When the dual-stream structure was replaced with the single-stream U-Net, the experimental results revealed a marked decline in both PSNR and SSIM values (PSNR dropped from 38.92 to 32.51), indicating that the edge-enhanced input stream significantly enhanced the model's ability to capture detailed information. The dual-stream architecture,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **FLOPs (G) \(\downarrow\)** & **Params (M) \(\downarrow\)** & **S/img \(\downarrow\)** \\ \hline SteganoGAN & 14.470 & 0.075 & 0.0418 \\ FNNS & 10.695 & 0.060 & 0.1318 \\ LISO & 408.770 & 0.197 & 0.5993 \\ Ours & 330.353 & 2.502 & 0.4594 \\ \hline \hline \end{tabular} Note: \(\downarrow\) signifies that lower values are preferable.  
\end{table}
Table 7: Comparison of computational complexity with other steganographic algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **JPEG(80)** & \
\begin{tabular}{c} **Gaussian Noise** \\ **Additive** \\ \end{tabular} &
\begin{tabular}{c} **Gaussian Noise** \\ **Multiplicative** \\ \end{tabular} \\ \hline Error Rate (\%) & \(3\times 10^{-3}\) & \(7\times 10^{-4}\) & \(2\times 10^{-4}\) \\ SSIM & 0.883 & 0.919 & 0.931 \\ PSNR & 33.57 & 34.89 & 35.56 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Robustness performance 1 bit on Div2k.  
by combining color information and edge features, improved both the visual quality and the information integrity of the stego image. Table 9 summarizes the experimental results for different ablation configurations. As shown in the table, the removal of these modules led to varying degrees of performance degradation across metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and error rate, further underscoring the essential role these modules play in the model.  
To evaluate the effectiveness of the multi-scale median attention (MSMA) module, we conducted a controlled ablation experiment. First, we constructed a dual-stream baseline model, excluding the influence of the InceptionDWK module. Based on this baseline, we replaced the attention module in turn with the CBAM [37] and ECA [46] attention mechanisms. Using the same training strategy, we performed comparative validation on a standard dataset. As shown in Table 10, quantitative results demonstrate that MSMA achieves significant improvements in PSNR, SSIM, and reconstruction error.