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Early neural approaches

# RNN-Stega: Linguistic Steganography Based on Recurrent Neural Networks

Zhong-Liang Yang, Xiao-Qing Guo, Zi-Ming Chen, Yong-Feng Huang, Yu-Jin Zhang

First online publication (IEEE): September 24, 2018 · IEEE TIFS 2019 (online 2018)

Published version: May 2019. The first online date and journal issue date are distinct.

Generates cover text with a recurrent language model and dynamically encodes candidate words using fixed-length or Huffman variable-length codes. It evaluates generation efficiency, embedding capacity, text quality, and resistance to the tested steganalysis methods.

[Read paper ↗](https://ieeexplore.ieee.org/document/8470163)

## Relationship to Neural Linguistic Steganography

The 2019 Neural Linguistic Steganography paper compares against this work's Huffman approach using the same GPT-2 language model as its own arithmetic method.

## Scope and limitations

The paper describes a capacity–quality trade-off and the cost of rebuilding a Huffman tree at each generation step. These empirical results should not be read as a distribution-preservation guarantee.

## Cite this paper

### Published version

Zhong-Liang Yang, Xiao-Qing Guo, Zi-Ming Chen, Yong-Feng Huang, Yu-Jin Zhang. [RNN-Stega: Linguistic Steganography Based on Recurrent Neural Networks](https://ieeexplore.ieee.org/document/8470163). IEEE Transactions on Information Forensics and Security, 2019

[Published version BibTeX](https://steganography.live/reading-list/papers/rnn-stega/publication.bib) · [Published version CSL-JSON](https://steganography.live/reading-list/papers/rnn-stega/publication.csl.json)
Show BibTeX`@article{rnn-stega-publication,
  title = {{RNN-Stega: Linguistic Steganography Based on Recurrent Neural Networks}},
  author = {Yang, Zhong-Liang and Guo, Xiao-Qing and Chen, Zi-Ming and Huang, Yong-Feng and Zhang, Yu-Jin},
  year = {2019},
  journal = {IEEE Transactions on Information Forensics and Security},
  doi = {10.1109/TIFS.2018.2871746},
  pages = {1280--1295},
  volume = {14},
  number = {5},
  publisher = {IEEE},
  url = {https://ieeexplore.ieee.org/document/8470163}
}
`

## Primary sources

- [Publisher record: first online publication on September 24, 2018; May 2019 journal issue, pages, and DOI.](https://ieeexplore.ieee.org/document/8470163)

- [Publisher-deposited complete author spellings, title, May 2019 issue metadata, DOI.](https://api.crossref.org/works/10.1109/TIFS.2018.2871746)

- [Full manuscript explicitly uploaded by author Zhongliang Yang; section III-B gives FLC/VLC and Huffman rebuilding trade-off; section IV gives evaluations.](https://www.researchgate.net/publication/327852903_RNN-Stega_Linguistic_Steganography_Based_on_Recurrent_Neural_Networks)

- [Sections 2 and 4 describe the Huffman comparison and shared-model reimplementation.](https://aclanthology.org/D19-1115.pdf)

- [Author-lab description of the RNN-Stega project.](https://raw.githubusercontent.com/YangzlTHU/Linguistic-Steganography-and-Steganalysis/master/Steganography/RNN-Stega/README.md)

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