Attacks on lexical natural language steganography systems

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Author

CM Taskiran, U Topkara, M Topkara, EJ Delp

Entry type

inproceedings

Abstract

Text data forms the largest bulk of digital data that people encounter and exchange daily. For this reason the potential usage of text data as a covert channel for secret communication is an imminent concern. Even though information hiding into natural language text has started to attract great interest, there has been no study on attacks against these applications. In this paper we examine the robustness of lexical steganography systems.In this paper we used a universal steganalysis method based on language models and support vector machines to differentiate sentences modified by a lexical steganography algorithm from unmodified sentences. The experimental accuracy of our method on classification of steganographically modified sentences was 84.9%. On classification of isolated sentences we obtained a high recall rate whereas the precision was low.

Date

2006 – 02

Booktitle

Security, Steganography, and Watermarking of Multimedia Contents VIII

Key alpha

Delp

Note

Proceedings of the SPIE

Pages

97-105

Volume

6072

Publication Date

2006-02-01

BibTex-formatted data

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@Inproceedings{ Delp,
	title = "Attacks on lexical natural language steganography systems",
	author = "CM Taskiran, U Topkara, M Topkara, EJ Delp",
	year = "2006",
	month = "02",
	booktitle = "Security, Steganography, and Watermarking of Multimedia Contents VIII",
	note = "Proceedings of the SPIE",
	pages = "97-105",
	volume = "6072",
	abstract = "Text data forms the largest bulk of digital data that people encounter and exchange daily. For this reason the potential usage of text data as a covert channel for secret communication is an imminent concern. Even though information hiding into natural language text has started to attract great interest, there has been no study on attacks against these applications. In this paper we examine the robustness of lexical steganography systems.In this paper we used a universal steganalysis method based on language models and support vector machines to differentiate sentences modified by a lexical steganography algorithm from unmodified sentences. The experimental accuracy of our method on classification of steganographically modified sentences was 84.9%. On classification of isolated sentences we obtained a high recall rate whereas the precision was low.",
}