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Proceedings Paper

Attacks on lexical natural language steganography systems
Author(s): Cuneyt M. Taskiran; Umut Topkara; Mercan Topkara; Edward J. Delp
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Paper 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.

Paper Details

Date Published: 15 February 2006
PDF: 9 pages
Proc. SPIE 6072, Security, Steganography, and Watermarking of Multimedia Contents VIII, 607209 (15 February 2006); doi: 10.1117/12.649551
Show Author Affiliations
Cuneyt M. Taskiran, Motorola Labs. (United States)
Umut Topkara, Purdue Univ. (United States)
Mercan Topkara, Purdue Univ. (United States)
Edward J. Delp, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 6072:
Security, Steganography, and Watermarking of Multimedia Contents VIII
Edward J. Delp III; Ping Wah Wong, Editor(s)

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