Generative Image Steganography with Minimum-Distance Guidance

Image steganography conceals secret data within a digital image while preserving its innocent appearance. The advent of artificial intelligence generative models has given rise to a new paradigm known as generative image steganography, which hides secret data directly into the image generation process. However, existing generative image steganographic methods are typically only applicable to unquantized stego images, severely limiting their practicality in real-world scenarios. To address this limitation, we propose a generative image steganography with minimum-distance guidance based on a diffusion model, called MDStega. During the hiding phase, MDStega designs a secret data-driven residual image sampling mechanism, which establishes a dynamic mapping relationship between discrete secret data and continuous probability distributions, strictly preserving the distribution consistency between stego images and normally generated images. During the extraction phase, the minimum-distance guidance rule effectively suppresses the interference caused by stego image quantization on the extraction accuracy of secret data. Furthermore, MDStega does not require fine-tuning pre-trained models or training additional models, which significantly reduces computational overhead and training time. Experimental results demonstrate that MDStega is superior to state-of-the-art methods by not only ensuring secure concealment at 3 bits per pixel (bpp) in PNG format but also achieving a recovery accuracy of up to 99%, demonstrating strong practical potential.