Generative Image Steganography Based on Text-to-Image Multimodal Generative Model

Abstract

Image steganography, the technique of hiding secret messages within images, has recently advanced with generative image steganography, which hides messages during image creation. However, current generative steganography methods often face criticism for their low extraction accuracy and poor robustness—particularly their vulnerability to JPEG compression. To address these challenges, we propose a novel generative image steganography method based on the text-to-image multimodal generative model (StegaMGM). StegaMGM utilizes the initial random normalization distribution in the generative process of latent diffusion models (LDMs), the secret message is hidden in the generated image through message sampling, ensuring it follows the same probability distribution as typical image generative. The content of the stego image can also be controlled through the prompts. On the receiver side, using the shared prompt and diffusion inversion, can extract secret message with high accuracy. In the experimental section, we conducted detailed experiments to demonstrate the advantages of our proposed StegaMGM framework in extraction accuracy, resistance to JPEG compression, and security.