Text steganography on RNN-Generated lyrics
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...
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Format: | Article |
Language: | English |
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AIMS Press
2019-06-01
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Series: | Mathematical Biosciences and Engineering |
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Online Access: | https://www.aimspress.com/article/10.3934/mbe.2019271?viewType=HTML |
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author | Yongju Tong YuLing Liu Jie Wang Guojiang Xin |
author_facet | Yongju Tong YuLing Liu Jie Wang Guojiang Xin |
author_sort | Yongju Tong |
collection | DOAJ |
description | 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. |
first_indexed | 2024-04-13T07:55:58Z |
format | Article |
id | doaj.art-91bfdfea63a040a3968170ec74aced2a |
institution | Directory Open Access Journal |
issn | 1551-0018 |
language | English |
last_indexed | 2024-04-13T07:55:58Z |
publishDate | 2019-06-01 |
publisher | AIMS Press |
record_format | Article |
series | Mathematical Biosciences and Engineering |
spelling | doaj.art-91bfdfea63a040a3968170ec74aced2a2022-12-22T02:55:24ZengAIMS PressMathematical Biosciences and Engineering1551-00182019-06-011655451546310.3934/mbe.2019271Text steganography on RNN-Generated lyricsYongju Tong0YuLing Liu1Jie Wang2Guojiang Xin31. College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China1. College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China2. Department of Computer Science, University of Massachusetts Lowell, Lowell, M.A., 01854, USA3. College of Management and Information Engineering, Hunan University of Chinese Medicine, Changsha 410208, ChinaWe 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.https://www.aimspress.com/article/10.3934/mbe.2019271?viewType=HTMLtext steganographylyric generationrecurrent neural networkschar-rnnword-rnn |
spellingShingle | Yongju Tong YuLing Liu Jie Wang Guojiang Xin Text steganography on RNN-Generated lyrics Mathematical Biosciences and Engineering text steganography lyric generation recurrent neural networks char-rnn word-rnn |
title | Text steganography on RNN-Generated lyrics |
title_full | Text steganography on RNN-Generated lyrics |
title_fullStr | Text steganography on RNN-Generated lyrics |
title_full_unstemmed | Text steganography on RNN-Generated lyrics |
title_short | Text steganography on RNN-Generated lyrics |
title_sort | text steganography on rnn generated lyrics |
topic | text steganography lyric generation recurrent neural networks char-rnn word-rnn |
url | https://www.aimspress.com/article/10.3934/mbe.2019271?viewType=HTML |
work_keys_str_mv | AT yongjutong textsteganographyonrnngeneratedlyrics AT yulingliu textsteganographyonrnngeneratedlyrics AT jiewang textsteganographyonrnngeneratedlyrics AT guojiangxin textsteganographyonrnngeneratedlyrics |