Reversible generative steganography with distribution-preserving

Abstract Steganography aims to embed and extract secret information in digital media for enhancing information security, which is widely applied to covert communication, copyright and privacy protection, digital forensics, etc. To resist steganalysis detection, generative steganography is one of the most promising techniques with embedding secret information into a generated image. Although existing generative steganographic methods could perform well with low hiding capacity, most of them encode the secret information in non-distribution-preserving manners, leading to poor security performance against steganalyzers when hiding more secret information. Meanwhile, the secret information tends to be difficult to be extracted with these methods because the secret-to-image transformations are irreversible. To tackle these issues, in this paper, we propose a reversible generative steganography with distribution-preserving scheme, which is mainly composed of a secret message mapping strategy with distribution-preserving and a reversible Glow model. To improve the anti-detectability against steganalyzers, the message mapping strategy with distribution-preserving is customized to encode the secret information into latent vectors which follow the Gaussian distribution as they are usually done in typical image generation models. The Glow model is then trained with reversible transformation to map the latent vectors into the generated stego-images with information hiding. Owing to the distribution-preserving and reversibility of the message mapping and Glow model, the proposed generative steganographic method achieves superior security performance and accurate extraction of secret message. Extensive experimental results demonstrate that the proposed method outperforms several state-of-the-art methods in terms of information extraction accuracy and anti-detectability, especially for high hiding capacity (up to 4.0 bpp).