# On Information Hiding in Natural Language Systems

arXiv: https://arxiv.org/abs/2203.06512 · HTML: https://mlsanity.com/paper/2203.06512/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 12 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding](https://mlsanity.com/paper/2009.03015/) (2020)
- [Neural Linguistic Steganography](https://mlsanity.com/paper/1909.01496/) (2019)
- [Towards Robust and Privacy-preserving Text Representations](https://mlsanity.com/paper/1805.06093/) (2018)
- [Provably Secure Generative Linguistic Steganography](https://mlsanity.com/paper/2106.02011/) (2021)
- [TextHide: Tackling Data Privacy in Language Understanding Tasks](https://mlsanity.com/paper/2010.06053/) (2020)
- [Towards Near-imperceptible Steganographic Text](https://mlsanity.com/paper/1907.06679/) (2019)
- [Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model](https://mlsanity.com/paper/2104.09833/) (2021)
- [Low Anisotropy Sense Retrofitting (LASeR) : Towards Isotropic and Sense Enriched Representations](https://mlsanity.com/paper/2104.10833/) (2021)
- [Text Steganalysis with Attentional LSTM-CNN](https://mlsanity.com/paper/1912.12871/) (2019)

## Abstract (arXiv)

With data privacy becoming more of a necessity than a luxury in today's digital world, research on more robust models of privacy preservation and information security is on the rise. In this paper, we take a look at Natural Language Steganography (NLS) methods, which perform information hiding in natural language systems, as a means to achieve data security as well as confidentiality. We summarize primary challenges regarding the secrecy and imperceptibility requirements of these systems and propose potential directions of improvement, specifically targeting steganographic text quality. We believe that this study will act as an appropriate framework to build more resilient models of Natural Language Steganography, working towards instilling security within natural language-based neural models.