Robust and Diversified Image Steganography Without Embedding Through a Disentanglement Autoencoder

Image Steganography without Embedding (SWE) is an emerging data hiding paradigm. Instead of embedding a secret message into a container image, SWE synthesises a novel image by using the secret message as a latent code. Current SWE methods have achieved high synthesis quality and strong resistance to steganalysis tools. However, it remains challenging to apply the SWE due to two reasons: (i) lack of synthesis diversity and (ii) recovery of secret messages under malicious image attacks. In this paper, we present a novel SWE framework with a disentanglement autoencoder to tackle the above challenges. Specifically, the autoencoder disentangles an image into a structure and texture representation. Then, we exploit the stability of the structure representation to improve secret message recovery reliability, while increasing synthesis diversity by randomising texture representations and employing a chaotic system for structure randomisation to enhance its security. To further achieve a robust message recovery under malicious attacks, an adversarial learning strategy is introduced into our framework, which guarantees high recovery accuracy. Our method outperforms other state-of-the-art SWE methods in terms of synthesis quality, synthesis diversity and secret message recovery accuracy under various image attacks. The source code is publicly available athttps://github.com/Lemok00/RDI-SWE.