College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
Department of Computer Science, University of Massachusetts Lowell, Lowell, M.A., 01854, USA.
Math Biosci Eng. 2019 Jun 12;16(5):5451-5463. doi: 10.3934/mbe.2019271.
We present a Recurrent Neural Network (RNN) Encoder-Decoder model to generate Chinese pop music lyrics to hide secret information. In particular, on a given initial line of a lyric, we use the LSTM model to generate the next Chinese character or word to form a new line. In so doing, we generate the entire lyric from what has been generated so far. Using common lyric formats and rhymes we extracted, we generate lyrics embedded with secret information to meet the visual and pronunciation requirements. We carry out experiments and theoretical analysis, and show that lyrics generated by our method offer higher embedding capacities for steganography, which also look more natural than the existing steganography methods based on text generations.
我们提出了一个递归神经网络（RNN）编解码器模型，用于生成中文流行音乐歌词来隐藏秘密信息。具体来说，给定歌词的初始行，我们使用 LSTM 模型生成下一个中文字符或单词以形成新行。通过这样做，我们从前一个生成的结果中生成整首歌词。使用我们提取的常见歌词格式和韵律，我们生成嵌入秘密信息的歌词，以满足视觉和发音的要求。我们进行了实验和理论分析，结果表明，我们的方法生成的歌词在隐写术方面具有更高的嵌入能力，并且比现有的基于文本生成的隐写术方法看起来更自然。
