Steganalysis against substitution-based linguistic steganography based on context clusters

Linguistic steganalysis has been an increasing interest stimulated by the emerging research area of linguistic steganography during the past few years. However, due to limitations of computer natural language processing capability, linguistic steganalysis is a challenging task. Existing steganalysis methods are inefficient to analyze most substitution-based linguistic steganography methods which preserve the syntactic and semantic correctness of cover texts. This paper provides a new steganalysis scheme against substitution-based linguistic steganography based on context clusters. In this scheme, we introduce context clusters to estimate the context fitness and show how to use the statistics of context fitness values to distinguish between normal texts and stego texts. Finally, under this scheme, we present the steganalysis method for synonym substitution-based linguistic steganography. Our experimental results show that the proposed steganalysis method can analyze synonym substitution-based linguistic steganography efficiently and the steganalysis accuracy reaches as high as 98.86%.

In a text, a context cluster is defined as a composition including a substitution element (a part being substituted of the text) and some of the context elements (parts indicating the context of the text), wherein the elements have a correlation stronger than certain designated threshold. In the proposed steganalysis method, context clusters are used to estimate the context fitness and then distinguish between normal texts and stego texts.Figure optionsDownload as PowerPoint slideHighlights
► Steganalysis against substitution-based linguistic steganography is challenging.
► Context clusters are introduced to estimate the context fitness.
► This paper uses the statistics of context fitness values for steganalysis.
► The proposed method is competitive in accuracy to the previous research.