Attacks on lexical natural language steganography systems

Cuneyt M. Taskiran; Umut Topkara; Mercan Topkara; Edward J. Delp

doi:10.1117/12.649551

15 February 2006 Attacks on lexical natural language steganography systems

Cuneyt M. Taskiran, Umut Topkara, Mercan Topkara, Edward J. Delp

Proceedings Volume 6072, Security, Steganography, and Watermarking of Multimedia Contents VIII; 607209 (2006) https://doi.org/10.1117/12.649551
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

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.

Citation Download Citation

Cuneyt M. Taskiran, Umut Topkara, Mercan Topkara, and Edward J. Delp "Attacks on lexical natural language steganography systems", Proc. SPIE 6072, Security, Steganography, and Watermarking of Multimedia Contents VIII, 607209 (15 February 2006); https://doi.org/10.1117/12.649551

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available