# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 5 Conclusions  
In this paper, we propose a dual-stream U-Net-based image steganography network with edge enhancement to overcome the limitations of existing steganographic techniques in edge feature extraction and global feature fusion while improving the quality of stego images and decoding accuracy. Our dual-stream U-Net architecture integrates edge-enhanced images with multi-scale median attention, significantly enhancing the model's feature representation capability and information embedding efficiency. Furthermore, the proposed InceptionDMK module strengthens the network's multi-scale feature extraction, enabling steganographic information to be embedded more effectively in complex visual environments. These contributions collectively enhance the network's capability to balance high embedding capacity with minimal visual distortion and decoding errors. Experimental results demonstrate that our method generates high-quality stego images across multiple public datasets, achieving a decoding accuracy close to 100% and significantly outperforming existing approaches in terms of PSNR and SSIM. Compared to current techniques, our method maintains a high steganographic capacity while achieving lower error rates and superior visual quality, which is of great significance in the field of information security.  
Nevertheless, our study has some limitations. First, when handling high payload information, further improvements in steganographic capacity may be constrained. Additionally, the computational complexity of the model and parameter tuning require further optimization. Moreover, while the Sobel operator offers advantages in computational efficiency, stability, and adaptability, we acknowledge the significant potential of deep learning-based methods for complex edge detection tasks. In particular, approaches such as HED, DexiNed, RCN, and BDCN enable more precise edge extraction. In future work,  
Figure 7: ROC curve for hidden message detection.  
we plan to refine the network architecture to reduce computational overhead while exploring the application of steganographic techniques to other multimedia formats, such as video and audio. Additionally, future work will involve evaluating the model on security-sensitive datasets to validate its robustness in real-world applications while exploring the integration of advanced deep learning-based edge detection methods to further enhance edge feature extraction and steganographic performance. We believe that with continued advancements, our approach will introduce new possibilities and challenges for steganography in the field of information security.  
Conceptualization, Y.Z. and Z.L.; methodology, P.J. and Y.Z.; formal analysis, Y.Z.; investigation, Y.Z. and Z.L.; resources, P.J.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, P.J.; visualization, Y.Z.; supervision, P.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data availability StatementData are contained within the article.  
The authors would like to express their gratitude to the Graduate School and the College of Computer and Information Engineering at Nanjing Tech University for this research.  
The authors declare no conflicts of interest.