  Generative linguistic steganography mainly utilized language models and
applied steganographic sampling (stegosampling) to generate high-security
steganographic text (stegotext). However, previous methods generally lead to
statistical differences between the conditional probability distributions of
stegotext and natural text, which brings about security risks. In this paper,
to further ensure security, we present a novel provably secure generative
linguistic steganographic method ADG, which recursively embeds secret
information by Adaptive Dynamic Grouping of tokens according to their
probability given by an off-the-shelf language model. We not only prove the
security of ADG mathematically, but also conduct extensive experiments on three
public corpora to further verify its imperceptibility. The experimental results
reveal that the proposed method is able to generate stegotext with nearly
perfect security.
