# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 1 Introduction
Although error correction codes can improve information extraction accuracy, their application increases computational burden and may reduce steganographic capacity. [PERSON] et al. proposed LISO [19] in their research, a neural network-based iterative optimization approach for image steganography. LISO achieves zero error rates up to 3 bits per pixel without using error correction codes by learning optimization steps while maintaining proximity to the natural image manifold, though the visual quality of its steganographic images is relatively low. The existing deep learning-based steganography methods have made remarkable progress in information hiding, yet they still face several challenges. First, many approaches struggle with high error rates when recovering large volumes of embedded information, which is critical for practical applications. Second, the visual quality of stego images often remains suboptimal, especially when dealing with complex images, potentially raising suspicion and reducing security. Third, the limited ability to capture image edges and texture details restricts the effective extraction of high-level features, impacting both the quality and security of the stego image. Motivated by these challenges, this paper proposes a novel image steganography method based on LISO, aimed at addressing these critical issues. The primary contributions of this paper are as follows:We propose an innovative dual-stream U-Net architecture that enhances the model's ability to learn from the intricate edges and contours of the cover image. An edge-enhanced image is introduced as an additional input stream, which effectively boosts the model's capacity to capture edge and texture details. In the dual-stream structure, each input stream is connected via independent skip connections, ensuring the comprehensive fusion of multi-level features and addressing the objective of enhancing steganographic robustness and visual quality. 2. We propose a multi-scale median attention (MSMA) module. This module comprises three key components: median-enhanced pooling, channel blending, and hierarchical deep convolution. First, median-enhanced pooling combines average, max, and median pooling to preserve edge and structural details. Then, channel blending improves inter-channel information exchange. Finally, hierarchical deep convolution enables multi-scale feature extraction, enhancing feature representation and improving steganography quality and decoding accuracy. In subsequent experiments, it was shown that the module improves the quality of steganography, with a 2.5 dB increase in average PSNR and a reduction in Error Rate to 0. 3. We introduce the InceptionDMK module (Inception-style Depthwise Multi-Kernel Convolution), which extracts global features while maintaining the capture of local details. This module significantly improves the network's ability to extract multi-scale features and its computational efficiency. By leveraging the detailed features from the edge-enhanced image, the module facilitates efficient embedding and reliable decoding of steganographic information, even in complex image scenarios, thus providing robust technical support for information-hiding applications. The remainder of this paper is organized as follows. Section 2 introduces some related work on image steganography techniques. Section 3 provides a detailed explanation of the technical principles and implementation process of our proposed method. Section 4 explains the metrics for evaluating the model and conducts an extensive experimental analysis and assessment of the method. Section 5 summarizes our work.