Edge-Guided Dual-Stream U-Net for Secure Image Steganography

Steganography, a technique for concealing information, often faces challenges such as low decoding accuracy and inadequate extraction of edge and global features. To overcome these limitations, we propose a dual-stream U-Net framework with integrated edge enhancement for image steganography. Our main contributions include the adoption of a dual-stream U-Net structure in the encoder, integrating an edge-enhancement stream with the InceptionDMK module for multi-scale edge detail extraction, and incorporating a multi-scale median attention (MSMA) module into the original input stream to enhance feature representation. This dual-stream design promotes deep feature fusion, thereby improving the edge details and embedding capacity of stego images. Moreover, an iterative optimization strategy is employed to progressively refine the selection of cover images and the embedding process, achieving enhanced stego quality and decoding performance. Experiments show that our method produces high-quality stego images across multiple public datasets, achieving near-100% decoding accuracy. It also surpasses existing methods in visual quality metrics like PSNR and SSIM. This framework offers a promising approach for enhancing steganographic security in real-world applications such as secure communication and data protection.