U-StegNet: A Unified Deep Network for Image Steganography

Steganography can be mainly classified into two categories: modification-based and generation-based approaches. Modification-based steganography embeds secret messages by altering specific statistical properties of the cover image, offering relatively high capacity and robustness. However, such modifications also make the stego images more susceptible to be detected by steganalysis tools. In contrast, generation-based steganography, which directly generates them via generative models, provides higher security but suffers from limited hiding capacity and poor robustness. To address these limitations, we propose a unified deep network for image steganography (U-StegNet) that integrates the high capacity and robustness of modification-based steganography with the high security of generation-based steganography. Specifically, we design a dual stage hiding strategy. In the first stage, we utilize a generative model to produce an intermediate image that hides part of the secret message while preserving statistical properties consistent with natural images, thereby enhancing resistance to steganalysis. In the second stage, we design a U-Net-based encoder to hide the remaining secret message into the intermediate image, while simultaneously enhancing fine-grained visual details. Moreover, we introduce a message division module that adaptively allocates the message length for each stage based on the image content and capacity constraints, thereby improving both imperceptibility and robustness. Extensive experimental results demonstrate the effectiveness of our U-StegNet. It achieves a PSNR of 44.41 dB and an SSIM of 0.9968 on stego images, which demonstrates the high imperceptibility of our method. Furthermore, it maintains strong robustness across nine types of common attacks, with message extraction accuracy nearly above 98%.