Open AccessA novel linguistic steganalysis method for hybrid steganographic texts
Author(s) -
Yuanyuan Xu,
Liran Yang,
Tengyun Zhao,
Ping Zhong
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1873/1/012053
Subject(s) - steganalysis , steganography , computer science , embedding , artificial intelligence , context (archaeology) , focus (optics) , natural language processing , word (group theory) , word embedding , information hiding , image (mathematics) , pattern recognition (psychology) , linguistics , optics , biology , paleontology , philosophy , physics
Most of the existing linguistic steganalysis methods mainly focus on detecting steganographic texts which are generated by embedding secret information into a type of text medium using one steganographic algorithm. But in practical applications, a large number of the steganographic texts may be hybrid ones which are generated by embedding secret information into different types of text media using different steganographic algorithms. In this paper, inspired by transfer learning, a novel linguistic steganalysis method is proposed to detect hybrid steganographic texts. The proposed method first uses the pre-trained BERT language model to obtain initial context-dependent word representations. Then the extracted features are fed into attentional Long Short-Term Memory (LSTM) to get the final contextual representations of sentences. The experimental results show that the proposed method can better satisfy the practical application demands than the existing linguistic steganalysis methods.