@Article{jcs.2020.015010,
AUTHOR = {Enlu Li, Zhangjie Fu, Siyu Chen, Junfu Chen},
TITLE = {A Two-Stage Highly Robust Text Steganalysis Model},
JOURNAL = {Journal of Cyber Security},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {183--190},
URL = {http://www.techscience.com/JCS/v2n4/40715},
ISSN = {2579-0064},
ABSTRACT = {With the development of natural language processing, deep learning, 
and other technologies, text steganography is rapidly developing. However, 
adversarial attack methods have emerged that gives text steganography the ability 
to actively spoof steganalysis. If terrorists use the text steganography method to 
spread terrorist messages, it will greatly disturb social stability. Steganalysis 
methods, especially those for resisting adversarial attacks, need to be further 
improved. In this paper, we propose a two-stage highly robust model for text 
steganalysis. The proposed method analyzes and extracts anomalous features at 
both intra-sentential and inter-sentential levels. In the first phase, every sentence 
is first transformed into word vectors. To obtain a high dimensional sentence 
vector, we use Bi-LSTM to obtain feature information for all words in the sentence 
while retaining strong correlations. In the second phase, we input multiple 
sentences vectors into the GNN, from which we extract inter-sentential anomaly 
features and make a judgment as to whether the text contains secret messages. In 
addition, to improve the robustness of the model, we add adversarial examples to 
the training set to improve the robustness and generalization of the steganalysis 
model. Theoretically, our proposed method is more robust and more accurate in 
detection compared to existing methods.},
DOI = {10.32604/jcs.2020.015010}
}