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Proceedings Paper

ML detection of steganography
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Paper Abstract

Digital steganography is the art of hiding information in multimedia content, such that it remains perceptually and statistically unchanged. The detection of such covert communication is referred to as steganalysis. To date, steganalysis research has focused primarily on either, the extraction of features from a document that are sensitive to the embedding, or the inference of some statistical difference between marked and unmarked objects. In this work, we evaluate the statistical limits of such techniques by developing asymptotically optimal tests (Maximum Likelihood) for a number of side informed embedding schemes. The required probability density functions (pdf) are derived for Dither Modulation (DM) and Distortion-Compensated Dither Modulation (DC-DM/SCS) from an steganalyst's point of view. For both embedding techniques, the pdfs are derived in the presence and absence of a secret dither key. The resulting tests are then compared to a robust blind steganalytic test based on feature extraction. The performance of the tests is evaluated using an integral measure and receiver operating characteristic (ROC) curves.

Paper Details

Date Published: 21 March 2005
PDF: 12 pages
Proc. SPIE 5681, Security, Steganography, and Watermarking of Multimedia Contents VII, (21 March 2005); doi: 10.1117/12.587024
Show Author Affiliations
Mark T. Hogan, Univ. College Dublin (Ireland)
Neil J. Hurley, Univ. College Dublin (Ireland)
Guenole C. M. Silvestre, Univ. College Dublin (Ireland)
Felix Balado, Univ. College Dublin (Ireland)
Kevin M. Whelan, Univ. College Dublin (Ireland)


Published in SPIE Proceedings Vol. 5681:
Security, Steganography, and Watermarking of Multimedia Contents VII
Edward J. Delp; Ping W. Wong, Editor(s)

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