  Linguistic steganography (LS) conceals the presence of communication by
embedding secret information into a text. How to generate a high-quality text
carrying secret information is a key problem. With the widespread application
of deep learning in natural language processing, recent algorithms use a
language model (LM) to generate the steganographic text, which provides a
higher payload compared with many previous arts. However, the security still
needs to be enhanced. To tackle with this problem, we propose a novel
autoregressive LS algorithm based on BERT and consistency coding, which
achieves a better trade-off between embedding payload and system security. In
the proposed work, based on the introduction of the masked LM, given a text, we
use consistency coding to make up for the shortcomings of block coding used in
the previous work so that we can encode arbitrary-size candidate token set and
take advantages of the probability distribution for information hiding. The
masked positions to be embedded are filled with tokens determined by an
autoregressive manner to enhance the connection between contexts and therefore
maintain the quality of the text. Experimental results have shown that,
compared with related works, the proposed work improves the fluency of the
steganographic text while guaranteeing security, and also increases the
embedding payload to a certain extent.
