  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.
