Text Steganalysis Based on Capsule Network with Dynamic Routing

References

• S. Das, S. Das, B. Bandyopadhyay, S. Sanyal. Steganography and steganalysis: different approaches. arXiv, Nov. 2011. Available: https://arxiv.org/pdf/1111.3758.pdf.
   Google Scholar
• S. Chauhary, M. Dave, and A. Sanghi, “Text steganography based on feature coding method,” in AICTC’16: Proceedings of the International Conference on Advances in Information Communication Technology & Computing, New York, 2016, pp. 1–4.
   Google Scholar
• B. K. Ramakrishnan, P. K. Thandra, and A. V. S. M. Srinivasula, “Text steganography: A novel character-level embedding algorithm using font attribute,” Secur. Commun. Netw., Vol. 9, no. 18, pp. 6066–79, Feb. 2017. doi: 10.1002/sec.1757
   Web of Science ®Google Scholar
• H. Huanhuan, Z. Xin, Z. Weiming, and Y. Nenghai, “Adaptive text steganography by exploring statistical and linguistical distortion,” in 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC), IEEE, Shenzhen, 2017, pp. 145–50.
   Google Scholar
• M. Khairullah, “A novel text steganography system using font color of the invisible characters in Microsoft Word Documents,” in International Conference on Computer & Electrical Engineering, IEEE, Dubai, 2009, pp. 482–4.
   Google Scholar
• M. Shirali-Shahreza, “Text steganography by changing words spelling,” in 2008 10th International Conference on Advanced Communication Technology, IEEE, Gangwon-Do, 2008, pp. 1912–3.
   Google Scholar
• S. Mahato, D. K. Yadav, and D. Khan, “A novel approach to text steganography using font size of invisible space characters in microsoft word document,” in Intelligent Computing, Networking, and Informatics, Jan. 2014, pp. 1047–54.
   Google Scholar
• S. Roy, and M. Manasmita, “A novel approach to format based text steganography,” in Proceedings of the 2011 International Conference on Communication, Computing & Security, ICCCS 2011, Odisha, 2011, pp. 511–6.
   Google Scholar
• F. Zamir Mansor, A. Mustapha, and N. Samsudin, “Researcher’s perspective of substitution method on text steganography,” IOP Conf. Ser. Mater. Sci. Eng., Vol. 226, pp. 012092, Aug. 2017. doi: 10.1088/1757-899X/226/1/012092
   Google Scholar
• L. Xiang, X. Sun, G. Luo, and C. Gan, “Research on steganalysis for text steganography based on font format,” in Third International Symposium on Information Assurance and Security, IEEE, Manchester, 2007, pp. 490–5.
   Google Scholar
• H. Yang, and G. Cao, “Linguistic steganalysis based on meta features and immune mechanism,” Chin. J. Electron., Vol. 19, no. 4, pp. 661–6, 2010.
   Web of Science ®Google Scholar
• L. Xiang, X. Sun, G. Luo, and B. Xia, “Linguistic steganalysis using the features derived from synonym frequency,” Multimed. Tools. Appl., Vol. 71, no. 3, pp. 1893–911, 2014. doi: 10.1007/s11042-012-1313-8
   Web of Science ®Google Scholar
• L. Xiang, Y. Li, W. Hao, P. Yang, and X. Shen, “Reversible natural language watermarking using synonym substitution and arithmetic coding,” Comput. Mater. Continua, Vol. 55, pp. 541–59, 2018.
   Web of Science ®Google Scholar
• H. Miao, F. Wang, L. Huang, Z. Chen, and W. Yang, “A novel text steganography by context-based equivalent substitution,” in IEEE International Conference on Signal Processing, IEEE, Kunming, 2013, pp. 1–6.
   Google Scholar
• G. Luo, X. Sun, L. Xiang, Y. Liu, and C. Gan, “Steganalysis on synonym substitution steganography,” J. Comput. Res. Dev., Vol. 45, no. 10, pp. 1696–703, Oct. 2008.
   Google Scholar
• Y. Luo, Y. Huang, F. Li, and C. Chang, “Text steganography based. on Ci-poetry generation using Markov Chain model,” Ksii Trans. Internet Inf. Syst., Vol. 10, no. 9, pp. 4568–84, Sep. 2016.
   Web of Science ®Google Scholar
• Y. Luo, and Y. Huang, “Text steganography with high embedding rate: using recurrent neural networks to generate chinese classic poetry,” ACM Workshop on Information Hiding Multimedia Security, pp. 99–104, 2017.
   Google Scholar
• Z. Yang, S. Jin, Y. Huang, Y. Zhang, and H. Li, Automatically generate steganographic text based on markov model and huffman coding, arXiv, Nov. 2018. Available: https://arxiv.org/pdf/1811.04720.pdf.
   Google Scholar
• Z.-L. Yang, X.-Q. Guo, Z.-M. Chen, Y.-F. Huang, and Y.-J. Zhang, “RNN-stega: linguistic steganography based on recurrent neural networks,” IEEE Trans. Inf. Forensics Secur., Vol. 14, no. 5, pp. 1280–95, May. 2019. doi: 10.1109/TIFS.2018.2871746
   Web of Science ®Google Scholar
• J. J. Zhang, L. C. Wang, and H. J. Lin, “Coverless text information hiding method based on the rank map,” J. Internet Technol., Vol. 10039, pp. 145–55, Jul. 2017.
   Google Scholar
• Z. Yang, N. Wei, J. Sheng, Y. Huang, and Y. Zhang, TS-CNN: Text steganalysis from semantic space based on convolutional neural network, arXiv, Oct. 2018. Available: https://arxiv.org/pdf/1810.08136.pdf.
   Google Scholar
• J. Wen, X. Zhou, P. Zhong, and Y. Xue, “Convolutional neural network based text steganalysis,” IEEE Signal Process Lett., Vol. 26, no. 3, pp. 460–4, Mar. 2019. doi: 10.1109/LSP.2019.2895286
   Web of Science ®Google Scholar
• Z. Yang, Y. Huang, and Y. Zhang, “A fast and efficient text steganalysis method,” IEEE Signal Process Lett., Vol. 26, no. 4, pp. 627–31, Apr. 2019. doi: 10.1109/LSP.2019.2902095
   Web of Science ®Google Scholar
• S. Sabour, N. Frosst, and G. E. Hinton. Dynamic routing between capsules, arXiv, Oct. 2017. Available: https://arxiv.org/abs/1710.09829.pdf.
   Google Scholar
• Z. Chen, L. Huang, H. Miao, W. Yang, and P. Meng, “Steganalysis against substitution-based linguistic steganography based on context clusters,” Comput. Electr. Eng., Vol. 37, no. 6, pp. 1071–81, Nov. 2011. doi: 10.1016/j.compeleceng.2011.07.004
   Web of Science ®Google Scholar
• P. Meng, L. Hang, W. Yang, Z. Chen, and H. Zheng, “Linguistic steganography detection algorithm using statistical language model,” in International Conference on Information Technology & Computer Science. IEEE Computer Society, Kiev, 2009, pp. 540–3.
   Google Scholar
• R. Din, “A genetic-based model for text steganalysis approach,” Int. Conf. Adv. Inf. Technol. Lecture Notes Inf. Technol., Vol. 39, pp. 101–10, 2013.
   Google Scholar
• L. Xiang, J. Yu, C. Yang, D. Zeng, and X. Shen, “A word-embedding-based steganalysis method for linguistic steganography via synonym-substitution,” IEEE. Access., Vol. 6, pp. 64131–41, 2018. doi: 10.1109/ACCESS.2018.2878273
   Web of Science ®Google Scholar
• S. Samanta, S. Dutta, and G. Sanyal, “A real time text steganalysis by using statistical method,” in Engineering and Technology (ICETECH), 2016 IEEE International Conference on. IEEE, Coimbatore, 2016, pp. 264–268.
   Google Scholar
• L. Xiang, G. Guo, J. Yu, V. S. Sheng, and P. Yang, “A convolutional neural network-based linguistic steganalysis for synonym substitution steganography,” Math. Biosci. Eng., Vol. 17, no. 2, pp. 1041–58, 2020. doi: 10.3934/mbe.2020055
   Web of Science ®Google Scholar
• Z. Yang, K. Wang, J. Li, Y. Huang, and Y. Zhang, TS-RNN: text steganalysis based on recurrent neural networks, arXiv May. 2019. Available: https://arxiv.org/pdf/1905.13087.pdf
   Google Scholar
• W. Zhao, J. Ye, M. Yang, S. Zhang, and Z. Zhao, Investigating capsule networks with dynamic routing for text classification, arXiv, Mar. 2018. Available: https://arxiv.org/pdf/1804.00538.pdf.
   Google Scholar
• H. Ren, and H. Lu. Compositional coding capsule network with k-means routing for text classification, arXiv Oct. 2018. Available: https://arxiv.org/pdf/1810.09177.pdf.
   Google Scholar
• X. Zhang, P. Li, W. Jia, and H. Zhao, Multi-labeled Relation Extraction with Attentive Capsule Network, arXiv, Nov. 2018. Available: https://arxiv.org/pdf/1811.04354.pdf.
   Google Scholar
• N. Zhang, S. Deng, Z. Sun, X. Chen, W. Zhang, and H. Chen, Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction, arXiv, Dec. 2018. Available: https://arxiv.org/pdf/1812.11321.pdf.
   Google Scholar
• T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient Estimation of Word Representations in Vector Space, arXiv, Jan. 2013. Available: https://arxiv.org/pdf/1301.3781.pdf.
   Google Scholar
• A. Maas, R. Daly, P. Pham, D. Huang, A. Ng, and C. Potts, “Learning word vectors for sentiment analysis,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-volume 1. Association for Computational Linguistics, Portland, 2011. pp. 142–50.
   Google Scholar
• All the news Available at: https://www.kaggle.com/snapcrack/all-the-news/data.
   Google Scholar
• S. L. West, D. A. Savitz, G. Koch, B. L. Strom, H. A. Guess, and A. Hartzema, “Recall accuracy for prescription medications: self-report compared with database information,” Am. J. Epidemiol., Vol. 142, no. 10, pp. 1103–12, Dec. 1995. doi: 10.1093/oxfordjournals.aje.a117563
   PubMed Web of Science ®Google Scholar