Previous works about linguistic steganography such as synonym substitution and sampling-based methods usually manipulate observed symbols explicitly to conceal secret information, which may give rise to security risks. In this letter, in order to preclude straightforward operation on observed symbols, we explored generation-based linguistic steganography in latent space by means of encoding secret messages in the selection of implicit attributes (semanteme) of natural language. We proposed a novel framework of linguistic semantic steganography based on rejection sampling strategy. Concretely, we utilized controllable text generation model for embedding and semantic classifier for extraction. In experiments, a model based on CTRL and BERT is implemented for further quantitative assessment. Results reveal that our approach is able to achieve satisfactory efficiency as well as nearly perfect imperceptibility. Our code is available at https://github.com/YangzlTHU/Linguistic-Steganography-and-Steganalysis/tree/master/Steganography/Linguistic-Semantic-Steganography.

中文翻译：

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语言隐写术：从符号空间到语义空间

以前关于语言隐写术的工作，例如同义词替换和基于采样的方法，通常会显式地操纵观察到的符号来隐藏秘密信息，这可能会带来安全风险。在这封信中，为了排除对观察到的符号的直接​​操作，我们通过在自然语言的隐式属性（语义）选择中对秘密消息进行编码，探索了潜在空间中基于生成的语言隐写术。我们提出了一种基于拒绝采样策略的语言语义隐写术新框架。具体来说，我们利用可控的文本生成模型进行嵌入和语义分类器进行提取。在实验中，实现了基于 CTRL 和 BERT 的模型，以进行进一步的定量评估。结果表明，我们的方法能够达到令人满意的效率以及几乎完美的不可察觉性。我们的代码位于 https://github.com/YangzlTHU/Linguistic-Steganography-and-Steganalysis/tree/master/Steganography/Linguistic-Semantic-Steganography。