A Comprehensive Review on Deep Learning-Based Generative Linguistic Steganography
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-26876-2_61
Reference23 articles.
1. Yang, Z.-L., Zhang, S.-Y., Hu, Y.-T., Hu, Z.-W., Huang, Y.-F.: VAE-Stega: linguistic steganography based on variational auto-encoder. IEEE Trans. Inf. Forensics Secur. 16, 880–895 (2021). https://doi.org/10.1109/TIFS.2020.3023279
2. Zhou, X., Peng, W., Yang, B., Wen, J., Xue, Y., Zhong, P.: Linguistic steganography based on adaptive probability distribution. IEEE Trans. Dependable Secure Comput. 19, 2982–2997 (2021)
3. Alanazi, N., Khan, E., Gutub, A.: Efficient security and capacity techniques for Arabic text steganography via engaging Unicode standard encoding. Multimedia Tools Appl. 80(1), 1403–1431 (2020). https://doi.org/10.1007/s11042-020-09667-y
4. Xiang, L., Yang, S., Liu, Y., Li, Q., Zhu, C.: Novel Linguistic steganography based on character-level text generation. Mathematics 8(9), 1558 (2020). https://doi.org/10.3390/math8091558
5. Kang, H., Wu, H., Zhang, X.: Generative text steganography based on LSTM network and attention mechanism with keywords. Electron. Imaging 2020(4), 291-1–291-8 (2020)
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Differential privacy to mathematically secure fine-tuned large language models for linguistic steganography;Assurance and Security for AI-enabled Systems;2024-06-07
2. A novel method for linguistic steganography by English translation using attention mechanism and probability distribution theory;PLOS ONE;2024-01-02
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