  Linguistic steganography studies how to hide secret messages in natural
language cover texts. Traditional methods aim to transform a secret message
into an innocent text via lexical substitution or syntactical modification.
Recently, advances in neural language models (LMs) enable us to directly
generate cover text conditioned on the secret message. In this study, we
present a new linguistic steganography method which encodes secret messages
using self-adjusting arithmetic coding based on a neural language model. We
formally analyze the statistical imperceptibility of this method and
empirically show it outperforms the previous state-of-the-art methods on four
datasets by 15.3% and 38.9% in terms of bits/word and KL metrics, respectively.
Finally, human evaluations show that 51% of generated cover texts can indeed
fool eavesdroppers.
