Embedding-Optimized Steganography Based on Post-quantum Encryption and BPNN

Multiple image steganography is an important branch of information-hiding technology, which can send information hidden in carrier images securely through unsafe channels. With the emergence of quantum computing, classical cryptography faces challenges in maintaining information security. To meet the challenge, post-quantum cryptography is advocated to ensure the communication security of steganography in quantum environments. In this paper, we propose a mechanism that enhances image steganography. We use post-quantum encryption to encrypt the secret information and realize image steganography by combining adaptive load distribution in multiple images. Specifically, to address low maximum embedding ability after steganography in traditional steganographic algorithms, we use fuzzy C-means clustering algorithm () to segment the foreground and background regions of the images, and then use the LSB embedding algorithm to achieve the steganography of the carrier images. Moreover, we use a Back Propagation Neural Network () based on particle swarm optimization () to optimize the image quality after steganography. Experiments are conducted to verify that, with our mechanism, the steganography images have high image perception quality, and thus can better protect confidential information.