Towards Near-imperceptible Steganographic Text

Dai, Falcon Z.; Cai, Zheng

doi:10.48550/arxiv.1907.06679

Cited by 1 publication

(2 citation statements)

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“…Then they use a full binary tree (FLC) and a Huffman tree (VLC) to encode the conditional probability distribution of each word, and output corresponding words according to the information needs to be hidden, so as to realize the embedding of hidden information in the sentence generation precess. After that, Dai et al [19] and Ziegler et al [20] further improve the statistical language model and the coding method of conditional probability distribution, which can further optimize the conditional probability distribution of each word in the generated steganographic sentences.…”

Section: Related Workmentioning

confidence: 99%

“…The previous steganographic text generative model mainly encodes this conditional probability distribution of each word and to embed the secret information [9], [17], [19], [20]. But as we mentioned before, their common problem is that, with the increase of embedding rate, the model will gradually select words with lower conditional probability, thus reducing the quality of generated text.…”

Section: Sentence Generationmentioning

confidence: 99%

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

Yang,

Gong,

et al. 2020

Preprint

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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.

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Section: Related Workmentioning

confidence: 99%