Early neural approaches
RNN-Stega: Linguistic Steganography Based on Recurrent Neural Networks
First online publication (IEEE): · 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.
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. IEEE Transactions on Information Forensics and Security, 2019
Published version BibTeX · Published version CSL-JSON
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@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.
- Publisher-deposited complete author spellings, title, May 2019 issue metadata, DOI.
- Full manuscript explicitly uploaded by author Zhongliang Yang; section III-B gives FLC/VLC and Huffman rebuilding trade-off; section IV gives evaluations.
- Sections 2 and 4 describe the Huffman comparison and shared-model reimplementation.
- Author-lab description of the RNN-Stega project.
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