Skip to main content
SpringerLink
Log in

Linguistic Steganalysis Based on Clustering and Ensemble Learning in Imbalanced Scenario

  • Conference paper
  • First Online:
Digital Forensics and Watermarking (IWDW 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14511))

Included in the following conference series:

  • 36 Accesses

Abstract

With the rapid development of the Internet, more and more methods of text steganography have emerged. However, these methods are easily abused in public networks for malicious purposes, which poses a great threat to cyberspace security. At present, a large number of text steganalysis methods have been proposed to game with text steganography. However, existing methods typically assume a balanced class distribution. In reality, stego texts are far less than cover texts. How to accurately detect stego texts in massive texts becomes a challenge. In this paper, we propose a text steganalysis method based on an under-sample method and ensemble learning in imbalanced scenarios. Specifically, we introduce the thinking of clustering to under-sample the majority class samples (cover texts) based on the detection difficulty of the samples, in order to select samples with rich information. Ensemble learning is then used to ensemble the detection results of multiple base classifiers and guide the sampling process. We designed several experiments to test the detection performance of the proposed model. Experimental results show that the proposed model can effectively compensate for the deficiencies of existing methods, even in highly imbalanced datasets, the model can still detect stego texts effectively.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
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

References

  1. Barandela, R., Valdovinos, R.M., Sánchez, J.S.: New applications of ensembles of classifiers. Pattern Anal. Appl. 6, 245–256 (2003)

    Article  MathSciNet  Google Scholar 

  2. Chen, Z., Huang, L., Miao, H., Yang, W., Meng, P.: Steganalysis against substitution-based linguistic steganography based on context clusters. Comput. Electr. Eng. 37(6), 1071–1081 (2011)

    Article  Google Scholar 

  3. 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)

  4. Freund, Y.: Boosting a weak learning algorithm by majority. Inf. Comput. 121(2), 256–285 (1995)

    Article  MathSciNet  Google Scholar 

  5. Galar, M., Fernández, A., Barrenechea, E., Herrera, F.: EUSBoost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recogn. 46(12), 3460–3471 (2013)

    Article  Google Scholar 

  6. Gao, L., Zhang, L., Liu, C., Wu, S.: Handling imbalanced medical image data: a deep-learning-based one-class classification approach. Artif. Intell. Med. 108, 101935 (2020)

    Article  Google Scholar 

  7. He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322–1328. IEEE (2008)

    Google Scholar 

  8. Huang, Y.F., Tang, S., Yuan, J.: Steganography in inactive frames of VoIP streams encoded by source codec. IEEE Trans. Inf. Forensics Secur. 6(2), 296–306 (2011)

    Article  Google Scholar 

  9. Johnson, N.F., Sallee, P.A.: Detection of hidden information, covert channels and information flows. In: Wiley Handbook of Science and Technology for Homeland Security, pp. 1–37 (2008)

    Google Scholar 

  10. Laurikkala, J.: Improving identification of difficult small classes by balancing class distribution. In: Quaglini, S., Barahona, P., Andreassen, S. (eds.) AIME 2001. LNCS, vol. 2101, pp. 63–66. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-48229-6_9

    Chapter  Google Scholar 

  11. Li, S., Wang, J., Liu, P.: Detection of generative linguistic steganography based on explicit and latent text word relation mining using deep learning. IEEE Trans. Dependable Secure Comput. 20(2), 1476–1487 (2022)

    Article  Google Scholar 

  12. Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(2), 539–550 (2008)

    Google Scholar 

  13. Liu, Y., Chawla, N.V., Harper, M.P., Shriberg, E., Stolcke, A.: A study in machine learning from imbalanced data for sentence boundary detection in speech. Comput. Speech Lang. 20(4), 468–494 (2006)

    Article  Google Scholar 

  14. Liu, Z., Wei, P., Jiang, J., Cao, W., Bian, J., Chang, Y.: MESA: boost ensemble imbalanced learning with meta-sampler. In: Advances in Neural Information Processing Systems, vol. 33, pp. 14463–14474 (2020)

    Google Scholar 

  15. Niu, Y., Wen, J., Zhong, P., Xue, Y.: A hybrid R-BILSTM-C neural network based text steganalysis. IEEE Sig. Process. Lett. 26(12), 1907–1911 (2019)

    Article  Google Scholar 

  16. Samanta, S., Dutta, S., Sanyal, G.: A real time text steganalysis by using statistical method. In: 2016 IEEE International Conference on Engineering and Technology (ICETECH), pp. 264–268. IEEE (2016)

    Google Scholar 

  17. Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum. 40(1), 185–197 (2009)

    Article  Google Scholar 

  18. Sun, B., Chen, H., Wang, J., Xie, H.: Evolutionary under-sampling based bagging ensemble method for imbalanced data classification. Front. Comput. Sci. 12, 331–350 (2018)

    Article  Google Scholar 

  19. Sun, Z., Song, Q., Zhu, X., Sun, H., Xu, B., Zhou, Y.: A novel ensemble method for classifying imbalanced data. Pattern Recogn. 48(5), 1623–1637 (2015)

    Article  Google Scholar 

  20. Tang, W., Li, B., Tan, S., Barni, M., Huang, J.: CNN-based adversarial embedding for image steganography. IEEE Trans. Inf. Forensics Secur. 14(8), 2074–2087 (2019)

    Article  Google Scholar 

  21. Wang, Y., Zhang, W., Li, W., Yu, X., Yu, N.: Non-additive cost functions for color image steganography based on inter-channel correlations and differences. IEEE Trans. Inf. Forensics Secur. 15, 2081–2095 (2019)

    Article  Google Scholar 

  22. Wang, Y., Gan, W., Yang, J., Wu, W., Yan, J.: Dynamic curriculum learning for imbalanced data classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5017–5026 (2019)

    Google Scholar 

  23. Wu, H., Yi, B., Ding, F., Feng, G., Zhang, X.: Linguistic steganalysis with graph neural networks. IEEE Sig. Process. Lett. 28, 558–562 (2021)

    Article  Google Scholar 

  24. Xiang, L., Sun, X., Luo, G., Xia, B.: Linguistic steganalysis using the features derived from synonym frequency. Multimedia Tools Appl. 71, 1893–1911 (2014)

    Article  Google Scholar 

  25. Yang, H., Bao, Y., Yang, Z., Liu, S., Huang, Y., Jiao, S.: Linguistic steganalysis via densely connected LSTM with feature pyramid. In: Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security, pp. 5–10 (2020)

    Google Scholar 

  26. Yang, H., Cao, X.: Linguistic steganalysis based on meta features and immune mechanism. Chin. J. Electron. 19(4), 661–666 (2010)

    Google Scholar 

  27. 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 

  28. 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 

  29. Yang, Z., Du, X., Tan, Y., Huang, Y., Zhang, Y.J.: AAG-Stega: automatic audio generation-based steganography. arXiv preprint arXiv:1809.03463 (2018)

  30. Yang, Z., Huang, Y., Zhang, Y.J.: A fast and efficient text steganalysis method. IEEE Sig. Process. Lett. 26(4), 627–631 (2019)

    Article  Google Scholar 

  31. Yang, Z., Huang, Y., Zhang, Y.J.: TS-CSW: text steganalysis and hidden capacity estimation based on convolutional sliding windows. Multimedia Tools Appl. 79, 18293–18316 (2020)

    Article  Google Scholar 

  32. Zhang, S., Yang, Z., Yang, J., Huang, Y.: Provably secure generative linguistic steganography. arXiv preprint arXiv:2106.02011 (2021)

  33. Zhou, F., et al.: Dynamic self-paced sampling ensemble for highly imbalanced and class-overlapped data classification. Data Min. Knowl. Disc. 36(5), 1601–1622 (2022)

    Article  MathSciNet  Google Scholar 

  34. Ziegler, Z.M., Deng, Y., Rush, A.M.: Neural linguistic steganography. arXiv preprint arXiv:1909.01496 (2019)

  35. Zou, J., Yang, Z., Zhang, S., ur Rehman, S., Huang, Y.: High-performance linguistic steganalysis, capacity estimation and steganographic positioning. In: Zhao, X., Shi, Y.Q., Piva, A., Kim, H.J. (eds.) IWDW 2020. LNSC, vol. 12617, pp. 80–93. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69449-4_7

    Chapter  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFC3303301 and in part by the National Natural Science Foundation of China under Grant 62172053 and Grant 62302059.

Author information

Authors and Affiliations

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

    Shengnan Guo, Xuekai Chen, Zhuang Wang, Zhongliang Yang & Linna Zhou

Authors
  1. Shengnan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuekai Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhuang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhongliang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Linna Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhongliang Yang .

Editor information

Editors and Affiliations

  1. School of Cyber Security, Qilu University of Technology, Jinan, China

    Bin Ma

  2. Qilu University of Technology, Jinan, China

    Jian Li

  3. Qilu University of Technology, Jinan, China

    Qi 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

Guo, S., Chen, X., Wang, Z., Yang, Z., Zhou, L. (2024). Linguistic Steganalysis Based on Clustering and Ensemble Learning in Imbalanced Scenario. In: Ma, B., Li, J., Li, Q. (eds) Digital Forensics and Watermarking. IWDW 2023. Lecture Notes in Computer Science, vol 14511. Springer, Singapore. https://doi.org/10.1007/978-981-97-2585-4_22

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-2585-4_22

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2584-7

  • Online ISBN: 978-981-97-2585-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation