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A General Linguistic Steganalysis Framework Using Multi-Task Learning

Lingyun Xiang1,*, Rong Wang1, Yuhang Liu1, Yangfan Liu1, Lina Tan2,3

1 School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China
2 School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK
3 School of Computer Science, Hunan University of Technology and Business, Changsha, 410205, China

* Corresponding Author: Lingyun Xiang. Email: email

Computer Systems Science and Engineering 2023, 46(2), 2383-2399.


Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts, by performing binary classification. While it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts coexist. In this paper, we propose a general linguistic steganalysis framework named LS-MTL, which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic texts. LS-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a constructed model. In the proposed framework, convolutional neural networks (CNNs) are utilized as private base models to extract sensitive features for each steganalysis task. Besides, a shared CNN is built to capture potential interaction information and share linguistic features among all tasks. Finally, LS-MTL incorporates the private and shared sensitive features to identify the detected text as steganographic or non-steganographic. Experimental results demonstrate that the proposed framework LS-MTL outperforms the baseline in the multi-category linguistic steganalysis task, while average Acc, Pre, and Rec are increased by 0.5%, 1.4%, and 0.4%, respectively. More ablation experimental results show that LS-MTL with the shared module has robust generalization capability and achieves good detection performance even in the case of spare data.


Cite This Article

APA Style
Xiang, L., Wang, R., Liu, Y., Liu, Y., Tan, L. (2023). A general linguistic steganalysis framework using multi-task learning. Computer Systems Science and Engineering, 46(2), 2383-2399.
Vancouver Style
Xiang L, Wang R, Liu Y, Liu Y, Tan L. A general linguistic steganalysis framework using multi-task learning. Comput Syst Sci Eng. 2023;46(2):2383-2399
IEEE Style
L. Xiang, R. Wang, Y. Liu, Y. Liu, and L. Tan "A General Linguistic Steganalysis Framework Using Multi-Task Learning," Comput. Syst. Sci. Eng., vol. 46, no. 2, pp. 2383-2399. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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