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A convolutional neural network-based linguistic steganalysis for synonym substitution steganography

1 Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, Hunan, China
2 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan, China
3 Hunan Provincial Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha 410114, Hunan, China
4 Department of Computer Science, University of Central Arkansas, Conway, AR, USA 72035
5 Hunan Branch of CNCERT/CC, Changsha 410004, Hunan, China

Special Issues: Information Multimedia Hiding & Forensics based on Intelligent Devices

In this paper, a linguistic steganalysis method based on two-level cascaded convolutional neural networks (CNNs) is proposed to improve the system’s ability to detect stego texts, which are generated via synonym substitutions. The first-level network, sentence-level CNN, consists of one convolutional layer with multiple convolutional kernels in different window sizes, one pooling layer to deal with variable sentence lengths, and one fully connected layer with dropout as well as a softmax output, such that two final steganographic features are obtained for each sentence. The unmodified and modified sentences, along with their words, are represented in the form of pre-trained dense word embeddings, which serve as the input of the network. Sentence-level CNN provides the representation of a sentence, and can thus be utilized to predict whether a sentence is unmodified or has been modified by synonym substitutions. In the second level, a text-level CNN exploits the predicted representations of sentences obtained from the sentence-level CNN to determine whether the detected text is a stego text or cover text. Experimental results indicate that the proposed sentence-level CNN can effectively extract sentence features for sentence-level steganalysis tasks and reaches an average accuracy of 82.245%. Moreover, the proposed steganalysis method achieves greatly improved detection performance when distinguishing stego texts from cover texts.
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Keywords steganalysis; steganography; synonym substitution; word embedding; convolutional neural network

Citation: Lingyun Xiang, Guoqing Guo, Jingming Yu, Victor S. Sheng, Peng Yang. A convolutional neural network-based linguistic steganalysis for synonym substitution steganography. Mathematical Biosciences and Engineering, 2020, 17(2): 1041-1058. doi: 10.3934/mbe.2020055

References

  • 1 Y. L. Liu, H. Peng and J. Wang, Verifiable diversity ranking search over encrypted outsourced data, CMC-Comput. Mater. Con., 55 (2018), 37-57.
  • 2 L. Y. Xiang, Y. Li, W. Hao, et al., Reversible natural language watermarking using synonym substitution and arithmetic coding, CMC-Comput. Mater. Con., 55 (2018), 541-559.
  • 3 H. M. Meral, B. Sankur, A. S. Ozsoy, et al., Natural language watermarking via morphosyntactic alterations, Comput. Speech Lang., 23 (2009), 107-125.
  • 4 C. M. Taskiran, M. Topkara and E. J. Delp, Attacks on lexical natural language steganography systems, Proceed. SPIE, 6072 (2006), 607209-607209-9.
  • 5 Z. L. Chen, L. S. Huang, H. B. Miao, et al., Steganalysis against substitution-based linguistic steganography based on context clusters, Comput. Electr. Eng., 37 (2011), 1071-1081.
  • 6 Z. L. Chen, L. S. Huang and W. Yang, Detection of substitution-based linguistic steganography by relative frequency analysis, Digit. Invest., 8 (2011), 68-77.
  • 7 L. Y. Xiang, X. M. Sun, G. Luo, et al., Linguistic steganalysis using the features derived from synonym frequency, Multimed. Tools Appl., 71 (2014), 1893-1911.
  • 8 L. Y. Xiang, J. M. Yu, C. F. Yang, et al., A word-embedding-based steganalysis method for linguistic steganography via synonym-substitution, IEEE Access, 6 (2018), 64131-64141.
  • 9 Z. S. Yu, L. S. Huang, Z. L. Chen, et al., Steganalysis of synonym-substitution based natural language watermarking, Int. J. Mult. Ubiquit. Eng., 4 (2012), 21-34.
  • 10 Z. S. Yu, L. S. Huang, Z. L. Chen, et al., Detection of synonym-substitution modified articles using context information, Second International Conference on Future Generation Communication and Networking, (2008), 134-139.
  • 11 Y. T. Chen, J. Xiong, W. H. Xu, et al., A novel online incremental and decremental learning algorithm based on variable support vector machine, Cluster Comput., Available from: https://doi.org/10.1007/s10586-018-1772-4.
  • 12 L. Y. Xiang, G. H. Zhao, Q. Li, et al., TUMK-ELM: A fast unsupervised heterogeneous data learning approach, IEEE Access, 6 (2018), 35305-35315.
  • 13 I. A. Bolshakov, A method of linguistic steganography based on collocationally-verified synonymy, International Workshop on Information Hiding, (2004), 180-191.
  • 14 C. Y. Chang and S. Clark, Practical linguistic steganography using contextual synonym substitution and a novel vertex coding method, Comput. Linguist., 40 (2014), 403-448.
  • 15 X. Yang, F. Li and L. Y. Xiang, Synonym substitution-based steganographic algorithm with matrix coding, J. Chinese Comput. Syst., 36 (2015), 1296-1300.
  • 16 H. H. Hu, X. Zuo, W. M. Zhang, et al., Adaptive text steganography by exploring statistical and linguistical distortion, IEEE Second International Conference on Data Science in Cyberspace, (2017), 145-150.
  • 17 O. Russakovsky, J. Deng, H. Su, et al., Imagenet large scale visual recognition challenge, Int. J. Comput. Vision, 115 (2015), 211-252.    
  • 18 L. Y. Xiang, X. B. Shen, J. H. Qin, et al., Discrete multi-graph hashing for large-scale visual search, Neural Process. Lett., 49 (2019), 1055-1069.
  • 19 J. Wang, J. H. Qin, X. Y. Xiang, et al., CAPTCHA recognition based on deep convolutional neural network, Math. Biosci. Eng., 16 (2019), 5851-5861.
  • 20 N. Kalchbrenner, E. Grefenstette and P. Blunsom, A convolutional neural network for modelling sentences, preprint, arXiv:1404.2188.
  • 21 D. J. Zeng, Y. Dai, F. Li, et al., Aspect based sentiment analysis by a linguistically regularized CNN with gated mechanism, J. Intell. Fuzzy Syst., 36 (2019), 3971-3980.    
  • 22 R. H. Meng, S. G. Rice, J. Wang, et al., A fusion steganographic algorithm based on faster R-CNN, CMC-Comput. Mater. Con., 55 (2018), 001-016.
  • 23 S. Q. Tan and B. Li, Stacked convolutional auto-encoders for steganalysis of digital images, Signal and Information Processing Association Summit and Conference, (2014), 1-4.
  • 24 J. Q. Ni, J. Ye and Y. Yang, Deep learning hierarchical representations for image steganalysis, IEEE T. Inf. Foren. Sec., 12 (2017), 2545-2557.
  • 25 Y. L. Qian, J. Dong, W. Wang, et al., Deep learning for steganalysis via convolutional neural networks, Proceed. SPIE, 9409 (2015), 94090J-94090J-10.
  • 26 G. S. Xu, H. Z. Wu and Y. Q. Shi, Structural design of convolutional neural networks for steganalysis, IEEE Signal Proc. Let., 23 (2016), 708-712.
  • 27 J. S. Zeng, S. Q. Tan, B. Li, et al., Large-scale jpeg image steganalysis using hybrid deep learning framework, IEEE T. Inf. Foren. Sec., 13 (2018), 1200-1214.
  • 28 A. Krizhevsky, I. Sutskever and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Proceedings of the 25th International Conference on Neural Information Processing Systems, 1 (2012), 1097-1105.
  • 29 Y. Kim, Convolutional neural networks for sentence classification, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, (2014), 1746-1751.
  • 30 J. Turian, L. Ratinov and Y. Bengio, Word representations: A simple and general method for semisupervised learning, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, (2010), 384-394.
  • 31 G. E. Hinton, Learning distributed representations of concepts, Proceedings of the Eighth Annual Conference of the Cognitive Science Society, 1 (1986), 12-23.
  • 32 Y. Bengio, H. Schwenk, J. Sencal, et al., Neural probabilistic language models, J. Mach. Learn. Res., 3 (2003), 1137-1155.
  • 33 F. Morin and Y. Bengio, Hierarchical probabilistic neural network language model, Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, (2005), 246-252.
  • 34 R. Collobert and J. Weston, A unified architecture for natural language processing: Deep neural networks with multitask learning, Proceedings of the 25th International Conference on Machine Learning, (2008), 160-167.
  • 35 T. Mikolov, I. Sutskever, K. Chen, et al., Distributed representations of words and phrases and their compositionality, International Conference on Neural Information Processing Systems, (2013), 3111-3119.
  • 36 B. Shen, C. W. Forstall, A. Rocha, et al., Practical text phylogeny for real-world settings, IEEE Access, 6 (2018), 41002-41012.
  • 37 D. J. Zeng, Y. Dai, F. Li, et al., Adversarial learning for distant supervised relation extraction, CMC-Comput. Mater. Con., 55 (2018), 121-136.
  • 38 R. Collobert, J. Weston, L. Bottou, et al., Natural language processing (almost) from scratch, J. Mach. Learn. Res., 12 (2011), 2493-2537.
  • 39 Y. L. Boureau, N. L. Roux, F. Bach, et al., Ask the locals: Multi-way local pooling for image recognition, 2011 International Conference on Computer Vision, (2011), 2651-2658.
  • 40 C. F. Yang, F. L. Liu, S. K. Ge, et al., Locating secret messages based on quantitative steganalysis, Math. Biosci. Eng., 16 (2019), 4908-4922.
  • 41 C. F. Yang, X. Y. Luo, J. C. Lu, et al., Extracting hidden messages of MLSB steganography based on optimal stego subset, Sci. China Inform. Sci., 61 (2018), 119103:1-119103:3.

 

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