A Highly Secure Steganography Scheme Based on Edge-aware Multi-scale Feature Decomposition

Image steganography, as one of the key technologies in digital communication, aims to achieve the secure exchange of secret information by imperceptibly embedding it into cover images. However, existing steganographic methods still face significant limitations in terms of security and visual quality. To address these challenges, this paper proposes a high-security image steganography model based on an improved Transformer architecture (AGDNet). By integrating innovative multi-scale feature convolution modules and a composite loss function design, the proposed model makes significant advancements in enhancing both the security and visual quality of steganographic information. Experimental results demonstrate that the proposed method exhibits superior performance across several key metrics, such as PSNR, SSIM, MAE, and RMSE, fully validating the effectiveness and advantages of AGDNet. In experiments conducted on the DIV2K dataset, the PSNR values for secret/recovered image pairs and cover/stego image pairs reached 58.80 and 57.64, respectively, outperforming existing state-of the-art steganographic methods. Moreover, AGDNet shows ex ceptional security in anti-steganalysis experiments, particularly when tested against five deep learning-based steganalysis models, where it significantly outperforms traditional methods. The experimental results indicate that AGDNet effectively enhances the concealment and security of steganographic information. https://github.com/SSSunShuaiSS/AGDNet