深度学习促进了自然语言处理技术的发展，基于文本生成的信息隐藏方法表现出了巨大的潜力.为此，提出了一种基于神经机器翻译的文本信息隐藏方法，在翻译文本生成过程中进行信息嵌入.神经机器翻译模型使用集束搜索（Beam Search）解码器，在翻译过程中通过Beam Search得到目标语言序列各位置上的候选单词集合，并将候选单词依据概率排序进行编码；然后在解码输出目标语言文本的过程中，根据秘密信息的二进制比特流选择对应编码的候选单词，实现以单词为单位的信息嵌入.实验结果表明，与已有的基于机器翻译的文本信息隐藏方法相比，该方法在隐藏容量方面明显提升，并且具有良好的抗隐写检测性和安全性.
Deep learning has promoted the development of natural language processing technology, and the information hiding methods based on text generation show great potential in this area. This paper proposes a text information hiding method based on neural machine translation, which embeds information in the process of generating translated text. The neural machine translation model uses a Beam Search decoder, which is used to obtain the candidate words in the sentence sequence in the translation process, and to encode the candidate words according to the probability ranking. Then, in the process of decoding and outputting target language texts, corresponding encoded candidate words are selected according to the binary bitstream of secret information, so as to realize information embedding at word level. Experimental results show that compared with the existing text information hiding methods based on machine translation, this method significantly improves the rate of information embedding, and shows good capability and security in anti-steganography performance.
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