  Most of the existing text generative steganographic methods are based on
coding the conditional probability distribution of each word during the
generation process, and then selecting specific words according to the secret
information, so as to achieve information hiding. Such methods have their
limitations which may bring potential security risks. Firstly, with the
increase of embedding rate, these models will choose words with lower
conditional probability, which will reduce the quality of the generated
steganographic texts; secondly, they can not control the semantic expression of
the final generated steganographic text. This paper proposes a new text
generative steganography method which is quietly different from the existing
models. We use a Knowledge Graph (KG) to guide the generation of steganographic
sentences. On the one hand, we hide the secret information by coding the path
in the knowledge graph, but not the conditional probability of each generated
word; on the other hand, we can control the semantic expression of the
generated steganographic text to a certain extent. The experimental results
show that the proposed model can guarantee both the quality of the generated
text and its semantic expression, which is a supplement and improvement to the
current text generation steganography.
