On Information Hiding in Natural Language Systems

TL;DR

This paper reviews natural language steganography methods for data security, discusses key challenges, and suggests future directions to improve the robustness and imperceptibility of steganographic texts in natural language systems.

Contribution

It provides a comprehensive overview of NLS challenges and proposes potential improvements to enhance security and text quality in natural language steganography.

Findings

Identifies core challenges in secrecy and imperceptibility

Highlights the need for resilient NLS models

Suggests future research directions

Abstract

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.