ML detection of steganography

Mark T. Hogan; Neil J. Hurley; Guenole C. M. Silvestre; Felix Balado; Kevin M. Whelan

doi:10.1117/12.587024

21 March 2005 ML detection of steganography

Mark T. Hogan, Neil J. Hurley, Guenole C. M. Silvestre, Felix Balado, Kevin M. Whelan

Proceedings Volume 5681, Security, Steganography, and Watermarking of Multimedia Contents VII; (2005) https://doi.org/10.1117/12.587024
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

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.

Citation Download Citation

Mark T. Hogan, Neil J. Hurley, Guenole C. M. Silvestre, Felix Balado, and Kevin M. Whelan "ML detection of steganography", Proc. SPIE 5681, Security, Steganography, and Watermarking of Multimedia Contents VII, (21 March 2005); https://doi.org/10.1117/12.587024

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