A Secure and Disambiguating Approach for Generative Linguistic Steganography

Yan, Ruiyi; Yang, Yating; Song, Tian

doi:10.1109/lsp.2023.3302749

IEEE Signal Process. Lett.

2023

DOI: 10.1109/lsp.2023.3302749

|View full text |Cite

A Secure and Disambiguating Approach for Generative Linguistic Steganography

Ruiyi Yan,

Yating Yang,

Tian Song

Help me understand this report

View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Crypto-space reversible data hiding for 3D mesh models with k-Degree neighbor diffusion

Gao,

Horng,

Chang

et al. 2025

Journal of Information Security and Applications

View full text Add to dashboard Cite

Crypto-space reversible data hiding for 3D mesh models with k-Degree neighbor diffusion

Gao,

Horng,

Chang

et al. 2025

Journal of Information Security and Applications

View full text Add to dashboard Cite

Linguistic Secret Sharing via Ambiguous Token Selection for IoT Security

Gao,

Horng,

Chang

et al. 2024

Electronics

View full text Add to dashboard Cite

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including weak authentication, insufficient data protection, and firmware vulnerabilities. To address these issues, we propose a linguistic secret sharing scheme tailored for IoT applications. This scheme leverages neural networks to embed private data within texts transmitted by IoT devices, using an ambiguous token selection algorithm that maintains the textual integrity of the cover messages. Our approach eliminates the need to share additional information for accurate data extraction while also enhancing security through a secret sharing mechanism. Experimental results demonstrate that the proposed scheme achieves approximately 50% accuracy in detecting steganographic text across two steganalysis networks. Additionally, the generated steganographic text preserves the semantic information of the cover text, evidenced by a BERT score of 0.948. This indicates that the proposed scheme performs well in terms of security.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Secure and Disambiguating Approach for Generative Linguistic Steganography

Cited by 2 publications

References 19 publications

Crypto-space reversible data hiding for 3D mesh models with k-Degree neighbor diffusion

Crypto-space reversible data hiding for 3D mesh models with k-Degree neighbor diffusion

Linguistic Secret Sharing via Ambiguous Token Selection for IoT Security

Contact Info

Product

Resources

About