Linguistic Steganalysis Based on Clustering and Ensemble Learning in Imbalanced Scenario

With the rapid development of the Internet, more and more methods of text steganography have emerged. However, these methods are easily abused in public networks for malicious purposes, which poses a great threat to cyberspace security. At present, a large number of text steganalysis methods have been proposed to game with text steganography. However, existing methods typically assume a balanced class distribution. In reality, stego texts are far less than cover texts. How to accurately detect stego texts in massive texts becomes a challenge. In this paper, we propose a text steganalysis method based on an under-sample method and ensemble learning in imbalanced scenarios. Specifically, we introduce the thinking of clustering to under-sample the majority class samples (cover texts) based on the detection difficulty of the samples, in order to select samples with rich information. Ensemble learning is then used to ensemble the detection results of multiple base classifiers and guide the sampling process. We designed several experiments to test the detection performance of the proposed model. Experimental results show that the proposed model can effectively compensate for the deficiencies of existing methods, even in highly imbalanced datasets, the model can still detect stego texts effectively.