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Rewriting-Stego: Generating Natural and Controllable Steganographic Text with Pre-trained Language Model

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Database Systems for Advanced Applications (DASFAA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13943))

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Abstract

Data transmission security and privacy play a crucial role in the era of information technology. Although the widely-used data encryption technique can ensure security, it can be easily detected and blocked by the observation system because the encrypted data format is quite different from the normal data. This work focuses on linguistic steganography, hiding a secret text in another normal stego text to ensure security and decrease the risk of being detected simultaneously. Rather than following the existing edit-based or generation-based paradigm, we propose a novel rewriting-based Rewriting-Stego, which tries to hide a secret text in the stego text by rewriting the given cover text. This paradigm integrates the advantages of both the edit-based paradigm and the generation-based paradigm, bringing higher information capacity without losing naturalness and controllability. Extensive experimental results on three public datasets have demonstrated the effectiveness of our Rewriting-Stego in terms of multiple metrics.

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Notes

  1. 1.

    Generally, the stego text is represented as a bit stream.

  2. 2.

    BART, 140M, https://huggingface.co/facebook/bart-base.

References

  1. Bernaille, L., Teixeira, R.: Early recognition of encrypted applications. In: Uhlig, S., Papagiannaki, K., Bonaventure, O. (eds.) PAM 2007. LNCS, vol. 4427, pp. 165–175. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71617-4_17

    Chapter  Google Scholar 

  2. Bi, X., Yang, X., Wang, C., Liu, J.: High-capacity image steganography algorithm based on image style transfer. Secur. Commun. Netw. 2021 ( 2021)

    Google Scholar 

  3. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL (2019)

    Google Scholar 

  4. Fang, T., Jaggi, M., Argyraki, K.J.: Generating steganographic text with LSTMs. In: ACL (2017)

    Google Scholar 

  5. Garg, M., Gupta, S., Khatri, P.: Fingerprint watermarking and steganography for ATM transaction using LSB-RSA and 3-DWT algorithm. In: ICCN, pp. 246–251 (2015)

    Google Scholar 

  6. Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N project report, Stanford 1(12) (2009)

    Google Scholar 

  7. Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691 (2021)

  8. Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: ACL (2020)

    Google Scholar 

  9. Ma, E.: Nlp augmentation (2019)

    Google Scholar 

  10. Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: ACL (2011)

    Google Scholar 

  11. Mikolov, T., Karafiát, M., Burget, L., Cernockỳ, J., Khudanpur, S.: Recurrent neural network based language model. In: Interspeech, vol. 2 (2010)

    Google Scholar 

  12. Qiu, X.P., Sun, T.X., Xu, Y.G., Shao, Y.F., Dai, N., Huang, X.J.: Pre-trained models for natural language processing: a survey. Sci. China Technol. Sci. 63(10), 1872–1897 (2020). https://doi.org/10.1007/s11431-020-1647-3

    Article  Google Scholar 

  13. Shen, J., Ji, H., Han, J.: Near-imperceptible neural linguistic steganography via self-adjusting arithmetic coding. In: EMNLP (2020)

    Google Scholar 

  14. Ueoka, H., Murawaki, Y., Kurohashi, S.: Frustratingly easy edit-based linguistic steganography with a masked language model. In: NAACL (2021)

    Google Scholar 

  15. Yang, Z., Guo, X., Chen, Z., Huang, Y., Zhang, Y.: RNN-stega: linguistic steganography based on recurrent neural networks. IEEE Trans. Inf. Forensics Secur. 14(5) (2019)

    Google Scholar 

  16. Zhang, C., Benz, P., Karjauv, A., Sun, G., Kweon, I.S.: UDH: universal deep hiding for steganography, watermarking, and light field messaging. In: NeurIPS (2020)

    Google Scholar 

  17. Ziegler, Z.M., Deng, Y., Rush, A.M.: Neural linguistic steganography. In: EMNLP-IJCNLP (2019)

    Google Scholar 

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grant 62162067 and 62101480, in part by the Yunnan Province Science Foundation under Grant No. 202005AC160007, No. 202001BB050076, and Research and Application of Object detection based on Artificial Intelligence, in part by the Applied Basic Research Foundation of Yunnan Province under Grant 202201AT070156, in part by the Fund project of Yunnan Province Education Department “Generating mnatural and controllable steganographic text based on language model”.

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Correspondence to Sixing Wu .

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Li, F. et al. (2023). Rewriting-Stego: Generating Natural and Controllable Steganographic Text with Pre-trained Language Model. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13943. Springer, Cham. https://doi.org/10.1007/978-3-031-30637-2_41

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  • DOI: https://doi.org/10.1007/978-3-031-30637-2_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30636-5

  • Online ISBN: 978-3-031-30637-2

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