Graph-Stega: Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph

Abstract

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.

Semantic-Preserving Linguistic Steganography by Pivot Translation and Semantic-Aware Bins Coding

“…However, for these methods, the term "imperceptibility" refers to language fluency as well as statistical similarity, rather than semantic consistency. Yang et al [15], [16] propose novel strategies to make the semantic expression conform to the context, yet expressing the accurate semantics remains impossible, making it difficult to adapt to realistic scenarios. Therefore, though GLS has the higher payload, semantic consistency is still a very challenging problem.…”