Linguistic steganography studies how to hide secret messages in natural
language cover texts. Traditional methods aim to transform a secret message
into an innocent text via lexical substitution or syntactical modification.
Recently, advances in neural language models (LMs) enable us to directly
generate cover text conditioned on the secret message. In this study, we
present a new linguistic steganography method which encodes secret messages
using self-adjusting arithmetic coding based on a neural language model. We
formally analyze the statistical imperceptibility of this method and
empirically show it outperforms the previous state-of-the-art methods on four
datasets by 15.3% and 38.9% in terms of bits/word and KL metrics, respectively.
Finally, human evaluations show that 51% of generated cover texts can indeed
fool eavesdroppers.

本研究提出了一种新的语言隐写方法，利用基于神经语言模型的自适应算术编码对秘密消息进行编码，并在四个数据集上验证了该方法的统计隐蔽性及其比之前的最先进方法在位数 / 单词和 KL 指标方面分别提高了 15.3% 和 38.9%，人类评估表明 51% 的生成的掩护文本可以愚弄窃听者。