Open Access
ARTICLE
A Two-Stage Highly Robust Text Steganalysis Model
Enlu Li1, Zhangjie Fu1,2,3,*, Siyu Chen1, Junfu Chen1
1 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 Guangxi Key Laboratory of Cryptography and Information Security, Guilin, 541004, China
3 College of Information Science and Technology, College of Cyber Security, Jinan University, Guangzhou, 510632, China
* Corresponding Author: Zhangjie Fu. Email:
Journal of Cyber Security 2020, 2(4), 183-190. https://doi.org/10.32604/jcs.2020.015010
Received 02 November 2020; Accepted 10 November 2020; Issue published 07 December 2020
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.
Keywords
Cite This Article
E. Li, Z. Fu, S. Chen and J. Chen, "A two-stage highly robust text steganalysis model,"
Journal of Cyber Security, vol. 2, no.4, pp. 183–190, 2020. https://doi.org/10.32604/jcs.2020.015010