基于神经机器翻译的文本隐写方法

doi:10.3969/j.issn.0255-8297.2020.06.014

Abstract

Abstract: 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.

Key words: text steganography, information hiding, neural network, machine translation

CLC Number:

TP391.41

YU Shuangsheng, YANG Zhongliang, JIANG Minyu, HUANG Yongfeng. Text Steganography Based on Neural Machine Translation[J]. Journal of Applied Sciences, 2020, 38(6): 976-985.

References

[1] Shannon C E. Communication theory of secrecy systems[J]. Bell System Technical Journal, 1949, 28(4):656-715.
[2] Bender W, Gruhl D, Morimoto N, et al. Techniques for data hiding[J]. IBM Systems Journal, 1996, 35(3):313-336.
[3] Brassil J T, Low S, Maxemchuk N F, et al. Marking text features of document images to deter illicit dissemination[J]//Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994, 3(2):315-319.
[4] Low S H, Maxemchuk N F, Brassil J, et al. Document marking and identification using both line and word shifting[C]//International Conference on Computer Communications, Boston, MA, USA, 1995:853-860.
[5] Bergmair R. Towards linguistic steganography:a systematic investigation of approaches, systems, and issues[J]. Derby:The University of Derby, 2004.
[6] Atallah M J, Mcdonough C J, Raskin V, et al. Natural language processing for information assurance and security:an overview and implementations[C]//Proceedings of the 2000 Workshop on New Security Paradigms, County Cork, Ireland, 2001:51-65.
[7] Dai W, Yu Y, Dai Y, et al. Text steganography system using Markov chain source model and DES algorithm[J]. Journal of Software, 2010, 5(7):785-792.
[8] Luo Y, Huang Y, Li F, et al. Text steganography based on Ci-poetry generation using Markov chain model[J]. KSⅡ Transactions on Internet and Information Systems, 2016, 10(9):4568-4584.
[9] Luo Y, Huang Y. Text steganography with high embedding rate:using recurrent neural networks to generate chinese classic poetry[C]//Proceedings of the 5th ACM workshop on information hiding and multimedia security. 2017:99-104.
[10] Fang T, Jaggi M, Argyraki K. Generating steganographic text with LSTMs[DB/OL]. arXiv preprint arXiv:1705.10742, 2017. DOI:10.18653/v1/P17-3017
[11] Yang Z L, Guo X Q, Chen Z M, et al. RNN-STEGA:linguistic steganography based on recurrent neural networks[J]. IEEE Transactions on Information Forensics and Security, 2018, 14(5):1280-1295.
[12] Grothoff C, Grothoff K, Alkhutova L, et al. Translation-based steganography[C]//International Workshop on Information Hiding. Heidelberg, Berlin:Springer, 2005:219-233.
[13] Stutsman R, Grothoff C, Atallah M, et al. Lost in just the translation[C]//Proceedings of the 2006 ACM symposium on Applied computing, Dijon, France, 2006:338-345.
[14] Meng P, Shi Y Q, Huang L, et al. LinL:Lost in n-best list[C]//International Workshop on Information Hiding. Heidelberg, Berlin:Springer, 2011:329-341.
[15] Sutskever I, Vinyals O, Le Q V, et al. Sequence to sequence learning with neural networks[J]. arXiv:Computation and Language, 2014.
[16] Bahdanau D, Cho K, Bengio Y, et al. Neural machine translation by jointly learning to align and translate[J]. arXiv:Computation and Language, 2014.
[17] Koehn P, Knowles R. Six challenges for neural machine translation[C]//Meeting of the Association for Computational Linguistics, Vancouver, Canada 2017:28-39.
[18] Press O, Smith N A. You maynot need attention[J]. arXiv preprint arXiv:1810.13409, 2018.
[19] Dyer C, Chahuneau V, Smith N A, et al. A simple, fast, and effective reparameterization of IBM model 2[C]//North American Chapter of the Association for Computational Linguistics, Atlanta, Georgia, USA 2013:644-648.
[20] Papineni K, Roukos S, Ward T, et al. Bleu:a method for automatic evaluation of machine translation[C]//Meeting of the Association for Computational Linguistics, Philadelphia, USA 2002:311-318.
[21] 孟朋. 自然语言信息隐藏与检测研究[D]. 合肥:中国科学技术大学, 2012.
[22] Chen Z, Huang L, Meng P, et al. Blind linguistic steganalysis against translation-based steganography[C]//International Workshop on Digital Watermarking, Berlin, Heidelberg:Springer, 2010:251-265.
[23] Yang Z, Huang Y, Zhang Y J. A fast and efficient text steganalysis method[J]. IEEE Signal Processing Letters, 2019, 26(4):627-631.

Text Steganography Based on Neural Machine Translation

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	MA Feihu, JIN Yichen, SUN Cuiyu. Short-Term Prediction Model of Subway Passenger Flow Based on EMD Optimized NAR Dynamic Neural Network [J]. Journal of Applied Sciences, 2020, 38(6): 936-943.
[2]	WANG Mengxuan, ZHANG Sheng, WANG Yue, LEI Ting, DU Wen. Research and Application of Improved CRNN Model in Classification of Alarm Texts [J]. Journal of Applied Sciences, 2020, 38(3): 388-400.
[3]	SUN Ronghai, SHI Linfu, YU Chunqiang, LAO Huan, TANG Zhenjun. Random Interpolation Method for Data Hiding in Encrypted Images [J]. Journal of Applied Sciences, 2020, 38(3): 419-430.
[4]	MA Xin, WU Yun, LU Zeguang. Collaborative Filtering Recommendation Model Based on Hybrid Neural Network [J]. Journal of Applied Sciences, 2020, 38(3): 478-487.
[5]	GAO Yifei, HU Yongjian, YU Zeqiong, LIN Yuyi, LIU Beibei. Evaluation and Comparison of Five Popular Fake Face Detection Networks [J]. Journal of Applied Sciences, 2019, 37(5): 590-608.
[6]	LIU Wei, ZHANG Wanling, XIANG Shijun. Face Anti-spoofing Based on LBP, Multilayer DCT and CNN [J]. Journal of Applied Sciences, 2019, 37(5): 609-617.
[7]	WANG Canjun, LIAO Xin, CHEN Jiaxin, QIN Zheng, LIU Xuchong. Research on Facial Modification Detection Algorithm Based on Convolutional Neural Network [J]. Journal of Applied Sciences, 2019, 37(5): 618-630.
[8]	LI Songbin, YANG Jie, LIU Peng, WANG Lingrui. Steganalysis of Motion Vector-Based Steganography in H.264/AVC by Correlation Network Model [J]. Journal of Applied Sciences, 2019, 37(5): 663-672.
[9]	ZHAO Yunying, SHAO Liping, WANG Yang, LU Hai. Improved Generation Camouflage Method Combined with Block Rotation and Photo Mosaic [J]. Journal of Applied Sciences, 2019, 37(5): 673-690.
[10]	ZHU Yiming, CHEN Fan, HE Hongjie, CHEN Hongyou. Orthogonal GAN Information Hiding Model Based on Secret Information Driven [J]. Journal of Applied Sciences, 2019, 37(5): 721-732.
[11]	WEI Wei, QUAN Haiyan. Radial Basis Network Training Algorithm Based on Surface-Simplex Swarm Evolution [J]. Journal of Applied Sciences, 2019, 37(4): 459-468.
[12]	JIN Huazhong, LIU Xiaolong, HU Zike. An Image Caption Generation Model Combining Global and Local Features [J]. Journal of Applied Sciences, 2019, 37(4): 501-509.
[13]	ZHAO Yunshan, DUAN Youxiang. Convolutional Neural Networks Text Classification Model Based on Attention Mechanism [J]. Journal of Applied Sciences, 2019, 37(4): 541-550.
[14]	YUAN Hong-lin, LU Xiao-dan, XU Chen. Estimation of Broadband Communication Transmitter Fingerprints Based on B-Spline Neural Network [J]. Journal of Applied Sciences, 2019, 37(1): 12-23.
[15]	ZENG Run-hua, ZHANG Shu-qun. Speech and Emotional Recognition Method Based on Improving Convolutional Neural Networks [J]. Journal of Applied Sciences, 2018, 36(5): 837-844.