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Proceedings Paper

Steganalysis-aware steganography: statistical indistinguishability despite high distortion
Author(s): Adem Orsdemir; H. Oktay Altun; Gaurav Sharma; Mark F. Bocko
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Paper Abstract

We consider the interplay between steganographer and the steganalyzer, and develop a steganalysis aware framework for steganography. The problem of determining a stego image is posed as a feasibility problem subject to constraint of data communication, imperceptibility, and statistical indistinguishability with respect to steganalyzer's features. A stego image is then determined using set theoretic feasible point estimation methods. The proposed framework is applied effectively on a state of the art steganalysis method based on higher order statistics (HOS) steganalysis. We first show that the steganographer can significantly reduce the classification performance of the steganalyzer by employing a statistical constraint during embedding, although the image is highly distorted. Then we show that steganalyzer can develop a counter-strategy against steganographer's action, gaining back some classification performance. This interchange represents an empirical iteration in this game between the steganographer and steganalyzer. Finally we consider mixture strategies to find the Nash equilibrium of the interplay.

Paper Details

Date Published: 18 March 2008
PDF: 9 pages
Proc. SPIE 6819, Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, 681915 (18 March 2008); doi: 10.1117/12.767402
Show Author Affiliations
Adem Orsdemir, Univ. of Rochester (United States)
H. Oktay Altun, Univ. of Rochester (United States)
Gaurav Sharma, Univ. of Rochester (United States)
Mark F. Bocko, Univ. of Rochester (United States)

Published in SPIE Proceedings Vol. 6819:
Security, Forensics, Steganography, and Watermarking of Multimedia Contents X
Edward J. Delp; Ping Wah Wong; Jana Dittmann; Nasir D. Memon, Editor(s)

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