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Security and Privacy in New Computing Environments. 6th International Conference, SPNCE 2023, Guangzhou, China, November 25–26, 2023, Proceedings

Research Article

Stable NICE Model-Based Picture Generation for Generative Steganography

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BibTeX Plain Text
  • @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
Xutong Cui1, Zhili Zhou2,*, Jianhua Yang3, Chengsheng Yuan1, Weixuan Tang2
  • 1: Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology
  • 2: Institute of Artificial Intelligence, Guangzhou University
  • 3: School of Cyber Security, Guangdong Polytechnic Normal University
*Contact email: zhou_zhili@163.com

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.

Keywords
Steganography Generative steganography Information hiding Digital forensics
Published
2025-01-01
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-73699-5_21
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