Skip to main content
SpringerLink
Log in

Hi-Stega: A Hierarchical Linguistic Steganography Framework Combining Retrieval and Generation

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14451))

Included in the following conference series:

  • 569 Accesses

Abstract

Due to the widespread use of social media, linguistic steganography which embeds secret message into normal text to protect the security and privacy of secret message, has been widely studied and applied. However, existing linguistic steganography methods ignore the correlation between social network texts, resulting in steganographic texts that are isolated units and prone to breakdowns in cognitive-imperceptibility. Moreover, the embedding capacity of text is also limited due to the fragmented nature of social network text. In this paper, in order to make the practical application of linguistic steganography in social network environment, we design a hierarchical linguistic steganography (Hi-Stega) framework. Combining the benefits of retrieval and generation steganography method, we divide the secret message into data information and control information by taking advantage of the fact that social network contexts are associative. The data information is obtained by retrieving the secret message in normal network text corpus and the control information is embedded in the process of comment or reply text generation. The experimental results demonstrate that the proposed approach achieves higher embedding payload while the imperceptibility and security can also be guaranteed. (All datasets and codes used in this paper are released at https://github.com/wanghl21/Hi-Stega.)

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Glove for Word Representation in https://github.com/stanfordnlp/GloVe.

  2. 2.

    It is the cosine distance of the sentence embedding using https://huggingface.co/sentence-transformers.

  3. 3.

    https://github.com/princeton-nlp/SimCSE.

References

  1. AlSabhany, A.A., Ali, A.H., Ridzuan, F., Azni, A., Mokhtar, M.R.: Digital audio steganography: systematic review, classification, and analysis of the current state of the art. Comput. Sci. Rev. 38, 100316 (2020)

    Article  Google Scholar 

  2. Anderson, R.J., Petitcolas, F.A.: On the limits of steganography. IEEE J. Sel. Areas Commun. 16(4), 474–481 (1998)

    Article  Google Scholar 

  3. Chang, C.Y., Clark, S.: Practical linguistic steganography using contextual synonym substitution and a novel vertex coding method. Comput. Linguist. 40(2), 403–448 (2014)

    Article  Google Scholar 

  4. Chen, X., Sun, H., Tobe, Y., Zhou, Z., Sun, X.: Coverless information hiding method based on the Chinese mathematical expression. In: Huang, Z., Sun, X., Luo, J., Wang, J. (eds.) ICCCS 2015. LNCS, vol. 9483, pp. 133–143. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27051-7_12

    Chapter  Google Scholar 

  5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  6. Gao, T., Yao, X., Chen, D.: SimCSE: simple contrastive learning of sentence embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6894–6910 (2021)

    Google Scholar 

  7. Jiang, T., et al.: PromptBERT: improving BERT sentence embeddings with prompts. arXiv preprint arXiv:2201.04337 (2022)

  8. Kadhim, I.J., Premaratne, P., Vial, P.J., Halloran, B.: Comprehensive survey of image steganography: techniques, evaluations, and trends in future research. Neurocomputing 335, 299–326 (2019)

    Article  Google Scholar 

  9. Kang, H., Wu, H., Zhang, X.: Generative text steganography based on LSTM network and attention mechanism with keywords. Electron. Imaging 2020(4), 291-1 (2020)

    Google Scholar 

  10. Kaptchuk, G., Jois, T.M., Green, M., Rubin, A.D.: Meteor: cryptographically secure steganography for realistic distributions. In: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, pp. 1529–1548 (2021)

    Google Scholar 

  11. Krishnan, R.B., Thandra, P.K., Baba, M.S.: An overview of text steganography. In: 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), pp. 1–6. IEEE (2017)

    Google Scholar 

  12. Liu, Y., Liu, S., Wang, Y., Zhao, H., Liu, S.: Video steganography: a review. Neurocomputing 335, 238–250 (2019)

    Article  Google Scholar 

  13. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

  14. Mikolov, T., Zweig, G.: Context dependent recurrent neural network language model. In: 2012 IEEE Spoken Language Technology Workshop (SLT), pp. 234–239. IEEE (2012)

    Google Scholar 

  15. Pascual, D., Egressy, B., Meister, C., Cotterell, R., Wattenhofer, R.: A plug-and-play method for controlled text generation. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 3973–3997 (2021)

    Google Scholar 

  16. Pillutla, K., et al.: MAUVE: measuring the gap between neural text and human text using divergence frontiers. In: Advances in Neural Information Processing Systems, vol. 34, pp. 4816–4828 (2021)

    Google Scholar 

  17. Radford, A., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)

    Google Scholar 

  18. Reinsel, D., Gantz, J., Rydning, J.: Data age 2025: the evolution of data to life-critical. Don’t Focus on Big Data 2 (2017)

    Google Scholar 

  19. Wai, E.N.C., Khine, M.A.: Modified linguistic steganography approach by using syntax bank and digital signature. Int. J. Inf. Educ. Technol. 1(5), 410 (2011)

    Google Scholar 

  20. Wang, F., Huang, L., Chen, Z., Yang, W., Miao, H.: A novel text steganography by context-based equivalent substitution. In: 2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013), pp. 1–6. IEEE (2013)

    Google Scholar 

  21. Wang, K., Gao, Q.: A coverless plain text steganography based on character features. IEEE Access 7, 95665–95676 (2019)

    Article  Google Scholar 

  22. Xiang, L., Wu, W., Li, X., Yang, C.: A linguistic steganography based on word indexing compression and candidate selection. Multimedia Tools Appl. 77(21), 28969–28989 (2018). https://doi.org/10.1007/s11042-018-6072-8

    Article  Google Scholar 

  23. Yang, J., Yang, Z., Zhang, S., Tu, H., Huang, Y.: SeSy: linguistic steganalysis framework integrating semantic and syntactic features. IEEE Sig. Process. Lett. 29, 31–35 (2021)

    Article  Google Scholar 

  24. Yang, T., Wu, H., Yi, B., Feng, G., Zhang, X.: Semantic-preserving linguistic steganography by pivot translation and semantic-aware bins coding. IEEE Trans. Dependable Secure Comput. (2023)

    Google Scholar 

  25. Yang, Z., Xu, C., Wu, W., Li, Z.: Read, attend and comment: a deep architecture for automatic news comment generation. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5077–5089 (2019)

    Google Scholar 

  26. Yang, Z.L., Guo, X.Q., Chen, Z.M., Huang, Y.F., Zhang, Y.J.: RNN-Stega: linguistic steganography based on recurrent neural networks. IEEE Trans. Inf. Forensics Secur. 14(5), 1280–1295 (2018)

    Article  Google Scholar 

  27. Yang, Z.L., Zhang, S.Y., Hu, Y.T., Hu, Z.W., Huang, Y.F.: VAE-Stega: linguistic steganography based on variational auto-encoder. IEEE Trans. Inf. Forensics Secur. 16, 880–895 (2020)

    Article  Google Scholar 

  28. Yang, Z., Wei, N., Sheng, J., Huang, Y., Zhang, Y.J.: TS-CNN: text steganalysis from semantic space based on convolutional neural network. arXiv preprint arXiv:1810.08136 (2018)

  29. Yang, Z., Xiang, L., Zhang, S., Sun, X., Huang, Y.: Linguistic generative steganography with enhanced cognitive-imperceptibility. IEEE Sig. Process. Lett. 28, 409–413 (2021)

    Article  Google Scholar 

  30. Zhang, J., Xie, Y., Wang, L., Lin, H.: Coverless text information hiding method using the frequent words distance. In: Sun, X., Chao, H.-C., You, X., Bertino, E. (eds.) ICCCS 2017. LNCS, vol. 10602, pp. 121–132. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68505-2_11

    Chapter  Google Scholar 

  31. Zhang, S., Yang, Z., Yang, J., Huang, Y.: Linguistic steganography: from symbolic space to semantic space. IEEE Sig. Process. Lett. 28, 11–15 (2020)

    Article  Google Scholar 

  32. Zhang, S., Yang, Z., Yang, J., Huang, Y.: Provably secure generative linguistic steganography. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 3046–3055 (2021)

    Google Scholar 

  33. Ziegler, Z., Deng, Y., Rush, A.M.: Neural linguistic steganography. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 1210–1215 (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, 100084, China

    Huili Wang

  2. School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Zhongliang Yang

  3. Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China

    Jinshuai Yang, Yue Gao & Yongfeng Huang

  4. Zhongguancun Laboratory, Beijing, 100094, China

    Yongfeng Huang

Authors
  1. Huili Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhongliang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinshuai Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yue Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yongfeng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huili Wang .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Biao Luo

  2. Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, H., Yang, Z., Yang, J., Gao, Y., Huang, Y. (2024). Hi-Stega: A Hierarchical Linguistic Steganography Framework Combining Retrieval and Generation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14451. Springer, Singapore. https://doi.org/10.1007/978-981-99-8073-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-8073-4_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8072-7

  • Online ISBN: 978-981-99-8073-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation