Abstract
Linguistic steganalysis is a technique that discovering potentially hidden information embedded through using linguistically in plain text using. Varieties of syntax and multi-meanings of semantics for linguistics augment the difficulty of linguistic steganalysis intensely, thereby it is a challenge area. In this paper, we propose a novel steganalysis method for linguistics based on immune. This method has two attributions: i). basis statistical features of text are employed for blind steganalysis ii). immune technique is chosen to build a two-level detection mechanism to detect two categories of stego text respectively, one of which is Success-Stego-text and another is False-Stego-text. Appropriate detections are generated and preferable features are signed. Experiments prove the approach has higher accuracy than current steganalysis algorithms. Especially when the segment size of text is greater than 3kB, the accuracies of detecting for natural text and stego text are both more than 95%.
Highlights
Compared with the research of image, audio and video hidden detection, (Kraetzer et al, 2015; Choubassi & Moulin, 2015; Kocal et al, 2016; Pankajakshan, Vinod & Ho, 2016; Bohme, 2016) text based information hiding detection is still a new and challenging field
A new natural language detection algorithm is proposed based on the analysis of text features
The algorithm uses multiple element feature better expresses the text characteristics, and through the analysis of changes of the features, the hidden text is divided into SST and FST, respectively, after feature extraction are designed, the corresponding detector, so the detection algorithm in the text is relatively short time have a higher rate
Summary
Compared with the research of image, audio and video hidden detection, (Kraetzer et al, 2015; Choubassi & Moulin, 2015; Kocal et al, 2016; Pankajakshan, Vinod & Ho, 2016; Bohme, 2016) text based information hiding detection is still a new and challenging field. We need to: (1) from the text element consideration for the detection of the characteristic, the reason is that the hidden text in grammar, semantics and natural text similarity, but through the analysis of the text element, we found that the scope of their characteristics is different in many meta features This is because the generated hidden text, choose random generally more natural text, making it smaller element changes. A natural language hidden detection algorithm based on text meta feature and immune mechanism is proposed. The immune mechanism (Jacob et al, 2013) has been effectively applied to image hiding detection [20], and has achieved very good detection results This mechanism generates a set of detectors that contain several features that separate the natural text from the hidden text.
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