Journal
MATHEMATICAL BIOSCIENCES AND ENGINEERING
Volume 16, Issue 5, Pages 5451-5463Publisher
AMER INST MATHEMATICAL SCIENCES-AIMS
DOI: 10.3934/mbe.2019271
Keywords
text steganography; lyric generation; recurrent neural networks; Char-RNN; Word-RNN
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Funding
- National Natural Science Foundation of China [61872134, 61502242]
- Natural Science Foundation of Hunan Province [2018JJ2062, 2018JJ2301]
- National Key Research and Development Program [2017YFC1703306]
- Hunan Provincial 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property [2017TP1025]
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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.
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