Abstract
Steganography based on text auto-generation technology is a current topic with great promise and challenges. It has the advantages of large information hiding capacity compared with the modification-based text steganographic methods. The biggest challenge faced by previous methods is that they can hardly generate fluent steganographic texts, and only pay attention to the statistical distribution of individual sentences without considering the overall statistical distribution of all generated texts. This paper proposes a text steganography called GAN-TStega which based on generative adversarial networks (GANs). Firstly, we use strategy update algorithm to solve the problem that traditional GANs are difficult to generate discrete data. Through antagonistic training on different types of text datasets, GAN-TStega can generate high quality texts. Then, by encoding the conditional probability distribution of generator’s output at each iteration, GAN-TStega can achieve secret information hiding. Through this method, we achieve the statistical distribution fitting at the sentence level, thus enhancing the security of steganography system. Experiments show that our method has good performance.
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Shannon, C.E.: Communication theory of secrecy systems. Bell Labs Tech. J. 28(4), 656–715 (1949)
Petitcolas, F.A., Anderson, R.J., Kuhn, M.G.: Information hiding-a survey. Proc. IEEE 87(7), 1062–1078 (1999)
Bernaille, L., Teixeira, R.: Early recognition of encrypted applications. In: Uhlig, S., Papagiannaki, K., Bonaventure, O. (eds.) PAM 2007. LNCS, vol. 4427, pp. 165–175. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71617-4_17
Fridrich, J.: Steganography in Digital Media: Principles, Algorithms, and Applications. Cambridge University Press, New York (2009)
Marvel, L.M., Boncelet, C.G., Retter, C.T.: Spread spectrum image steganography. IEEE Trans. Image Process. 8(8), 1075–1083 (1999)
Luo, W., Huang, F., Huang, J.: Edge adaptive image steganography based on LSB matching revisited. IEEE Trans. Inf. Forensics Secur. 5(2), 201–214 (2010)
Yang, Z., Peng, X., Huang, Y.: A sudoku matrix-based method of pitch period steganography in low-rate speech coding. In: Lin, X., Ghorbani, A., Ren, K., Zhu, S., Zhang, A. (eds.) SecureComm 2017. LNICST, vol. 238, pp. 752–762. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78813-5_40
Yang, Z., Du, X., Tan, Y., Huang, Y., Zhang, Y.-J.: AAG-stega: automatic audio generation-based steganography. arXiv preprint arXiv:1809.03463 (2018)
Yang, Z.-L., Guo, X.-Q., Chen, Z.-M., Huang, Y.-F., Zhang, Y.-J.: RNN-stega: linguistic steganography based on recurrent neural networks. IEEE Trans. Inf. Forensics Secur. 14(5), 1280–1295 (2019)
Yang, Z., Jin, S., Huang, Y., Zhang, Y., Li, H.: Automatically generate steganographic text based on Markov model and Huffman coding. arXiv preprint arXiv:1811.04720 (2018)
Bennett, K.: Linguistic steganography: survey, analysis, and robustness concerns for hiding information in text (2004)
Xiang, L., Sun, X., Luo, G., Xia, B.: Linguistic steganalysis using the features derived from synonym frequency. Multimed. Tools Appl. 71(3), 1893–1911 (2014). https://doi.org/10.1007/s11042-012-1313-8
Yang, Z., Zhang, P., Jiang, M., Huang, Y., Zhang, Y.-J.: RITS: real-time interactive text steganography based on automatic dialogue model. In: Sun, X., Pan, Z., Bertino, E. (eds.) ICCCS 2018. LNCS, vol. 11065, pp. 253–264. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00012-7_24
Yang, Z., Zhang, Y.-J., Rehman, S., Huang, Y.: Image captioning with object detection and localization. In: Zhao, Y., Kong, X., Taubman, D. (eds.) ICIG 2017. LNCS, vol. 10667, pp. 109–118. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71589-6_10
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)
Dai, W., Yu, Y., Dai, Y., Deng, B.: Text steganography system using Markov chain source model and DES algorithm. JSW 5(7), 785–792 (2010)
Moraldo, H.H.: An approach for text steganography based on Markov chains. arXiv preprint arXiv:1409.0915 (2014)
Dai, W., Yu, Y., Deng, B.: BinText steganography based on Markov state transferring probability. In: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, pp. 1306–1311. ACM (2009)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 1171–1179 (2015)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: sequence generative adversarial nets with policy gradient. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Guo, J., Lu, S., Cai, H., Zhang, W., Yu, Y., Wang, J.: Long text generation via adversarial training with leaked information. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Fedus, W., Goodfellow, I., Dai, A.M.: MaskGAN: better text generation via filling in the\(\_\)," arXiv preprint arXiv:1801.07736 (2018)
Volkhonskiy, D., Nazarov, I., Borisenko, B., Burnaev, E.: Steganographic generative adversarial networks. arXiv preprint arXiv:1703.05502 (2017)
Hayes, J., Danezis, G.: Generating steganographic images via adversarial training. In: Advances in Neural Information Processing Systems, pp. 1954–1963 (2017)
Yang, Z., Wang, K., Li, J., Huang, Y., Zhang, Y.: TS-RNN: text steganalysis based on recurrent neural networks. IEEE Signal Process. Lett. 99, 1 (2019)
Chen, X., et al.: Microsoft COCO captions: data collection and evaluation server. arXiv preprint arXiv:1504.00325 (2015)
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579–2605 (2008)
Acknowledgment
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB0804103 and the National Natural Science Foundation of China (No. U1536207, No. U1705261 and No. U1636113).
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Yang, Z., Wei, N., Liu, Q., Huang, Y., Zhang, Y. (2020). GAN-TStega: Text Steganography Based on Generative Adversarial Networks. In: Wang, H., Zhao, X., Shi, Y., Kim, H., Piva, A. (eds) Digital Forensics and Watermarking. IWDW 2019. Lecture Notes in Computer Science(), vol 12022. Springer, Cham. https://doi.org/10.1007/978-3-030-43575-2_2
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