Research Article
Stable NICE Model-Based Picture Generation for Generative Steganography
@INPROCEEDINGS{10.1007/978-3-031-73699-5_21, author={Xutong Cui and Zhili Zhou and Jianhua Yang and Chengsheng Yuan and Weixuan Tang}, title={Stable NICE Model-Based Picture Generation for Generative Steganography}, proceedings={Security and Privacy in New Computing Environments. 6th International Conference, SPNCE 2023, Guangzhou, China, November 25--26, 2023, Proceedings}, proceedings_a={SPNCE}, year={2025}, month={1}, keywords={Steganography Generative steganography Information hiding Digital forensics}, doi={10.1007/978-3-031-73699-5_21} }- Xutong Cui
Zhili Zhou
Jianhua Yang
Chengsheng Yuan
Weixuan Tang
Year: 2025
Stable NICE Model-Based Picture Generation for Generative Steganography
SPNCE
Springer
DOI: 10.1007/978-3-031-73699-5_21
Abstract
Steganography is one of most important techniques for covert communication. In recent years, generative steganography, which transforms a secret information into a generated picture, is a prospective steganography-resistant technique. Nevertheless, it is difficult to achieve a good trade-off between information hiding ability and extraction accuracy because of the low efficiency and irreversibility of the secret-to-picture conversion. In order to solve this problem, this paper proposes a secret message-driven picture generation solution for generative steganography. The presented SM-IG scheme is founded on the design of a stable version of the Nearly Independent Component Estimation (Stable NICE) model, allowing for a stable bijection mapping between a potential space with simple distributions and an picture space with complex distributions. During the secret to picture conversion, a latent vector is constructed, driven by a given secret message, which is then mapped to the generated picture via the Stable NICE modelAs a result, the secret information is eventually converted into the generated picture. Due to the good efficiency and reversibility of the SM-IG scheme, this steganography method has high hiding capability and accurate message extraction accuracy. The experiments prove that the proposed SM-IG can simultaneously realise good-level hiding capacity (as much as 4 bpp) and precise extraction accuracy (close to 100(\%)accuracy) without compromising the required resistance to detection and imperceptibility.
