Detection of Synonym-Substitution Modified Articles Using Context Information

Text steganography usually modifies the cover-text (where secrets are embedded) in some meaning-preserving ways to conceal secret messages, while steganalysis does the opposite - detects or extracts the secrets. A lot of work has been done on steganography, but only a little on steganalysis. In this paper, we analyze one kind of text steganography that use synonym substitution. We try to distinguish between modified articles and unmodified articles using context information. We evaluate the suitability of words for their context, and then the suitability sequence of words leads to the final judgment made by a SVM (support vector machine) classifier. IDF (inverse document frequency) is used to weight words’ suitability in order to balance common words and rare ones. This scheme is evaluated on internet instead of in a specific corpus, with the help of Google. Experimental results show that classification accuracy achieves 90.0%.