Fixed Neural Network Image Steganography Based on Secure Diffusion Models

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.