Tongxin xuebao (Apr 2023)
Generative text steganography method based on emotional expression in semantic space
Abstract
Aiming at the problems that “over optimizing” the quality of steganographic text and lack of constraints on the semantic expression of the generated steganographic text in existing generative text steganography methods, a generative text steganography method was proposed based on emotional expression in semantic space.In order to make use of the scene fusion provided by the new media platform to obtain many camouflage scenes, the focus was how to use the unsupervised extraction model to extract the emotional expression combination candidate set from the original data set, then sort the candidate set of emotional expression combinations based on the improved bipartite graph sorting algorithm to obtain the emotional expression combination set, map them to the semantic space, and then implement embedding secret information while generating the user’s opinions based on the emotion expression combinations.Experimental results show that, compared with the existing generative text steganography methods in semantic space, the product reviews generated by the proposed method have a minimum perplexity of 10.536, and have a strong correlation with the chosen product, which can further guarantee the cognitive concealment of steganographic texts.At the same time, the proposed method can also be effectively used in the field of secure and confidential communication, and can avoid the senders being traced and analyzed.
