A novel linguistic steganalysis method for hybrid steganographic texts

Xu, Yuanyuan; Yang, Liran; Zhao, Tengyun; Zhong, Ping

doi:10.1088/1742-6596/1873/1/012053

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…Subsequently, Zou et al [27] employed Bidirectional Encoder Representation from Transformers (BERT) and Global Vectors for Word Representation (Glove) to capture inter-sentence contextual association relationships, then extracted context information using Bi-LSTM and finally obtained the sensitive semantic features via the attention mechanism for steganographic text detection. Xu et al [28] employed a pre-trained BERT language model to obtain initial contextually relevant word representation, after which the extracted features were fed into an LSTM with attention to obtain the final sentence representation used to classify the detected texts. Besides, [28] also mixes the steganographic texts generated by several steganographic methods.…”

Section: Related Workmentioning

confidence: 99%

“…Xu et al [28] employed a pre-trained BERT language model to obtain initial contextually relevant word representation, after which the extracted features were fed into an LSTM with attention to obtain the final sentence representation used to classify the detected texts. Besides, [28] also mixes the steganographic texts generated by several steganographic methods.…”

Section: Related Workmentioning

confidence: 99%

“…BERT-LSTM [28] Conducting a three-category task, generative (T-stega [18]), modified stego texts (T-lex), and natural non-stego texts.…”

Section: Ls-cnn [12]mentioning

confidence: 99%

See 2 more Smart Citations

A General Linguistic Steganalysis Framework Using Multi-Task Learning

Xiang¹,

Wang²,

Liu³

et al. 2023

Computer Systems Science and Engineering

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%