Traditional steganography conceals information by modifying cover data, but steganalysis tools easily detect such alterations. While deep learning-based steganography often involves high training costs and complex deployment. Diffusion model-based methods face security vulnerabilities, particularly due to potential information leakage during generation. We propose a fixed neural network image steganography framework based on secure diffusion models to address these challenges. Unlike conventional approaches, our method minimizes cover modifications through neural network optimization, achieving superior steganographic performance in human visual perception and computer vision analyses. The cover images are generated in an anime style using state-of-the-art diffusion models, ensuring the transmitted images appear more natural. This study introduces fixed neural network technology that allows senders to transmit only minimal critical information alongside stego-images. Recipients can accurately reconstruct secret images using this compact data, significantly reducing transmission overhead compared to conventional deep steganography. Furthermore, our framework innovatively integrates ElGamal, a cryptographic algorithm, to protect critical information during transmission, enhancing overall system security and ensuring end-to-end information protection. This dual optimization of payload reduction and cryptographic reinforcement establishes a new paradigm for secure and efficient image steganography.

Until now, some reversible data hiding in encrypted images (RDH-EI) schemes based on secret sharing (SIS-RDHEI) still have the problems of not realizing diffusivity and high embedding capacity. Therefore, this paper innovatively proposes a high capacity RDH-EI scheme that combines adaptive most significant bit (MSB) prediction with secret sharing technology. Firstly, adaptive MSB prediction is performed on the original image and cryptographic feedback secret sharing strategy encrypts the spliced pixels to spare embedding space. In the data hiding phase, each encrypted image is sent to a data hider to embed the secret information independently. When r copies of the image carrying the secret text are collected, the original image can be recovered lossless and the secret information can be extracted. Performance evaluation shows that the proposed method in this paper has the diffusivity, reversibility, and separability. The last but the most important, it has higher embedding capacity. For 512 × 512 grayscale images, the average embedding rate reaches 4.7358 bits per pixel (bpp). Compared to the average embedding rate that can be achieved by the Wang et al.’s SIS-RDHEI scheme, the proposed scheme with (2, 2), (2, 3), (2, 4), (3, 4), and (3, 5)-threshold can increase by 0.7358 bpp, 2.0658 bpp, 2.7358 bpp, 0.7358 bpp, and 1.5358 bpp, respectively.