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Proceedings Paper

Steganalysis feature improvement using expectation maximization
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Paper Abstract

Images and data files provide an excellent opportunity for concealing illegal or clandestine material. Currently, there are over 250 different tools which embed data into an image without causing noticeable changes to the image. From a forensics perspective, when a system is confiscated or an image of a system is generated the investigator needs a tool that can scan and accurately identify files suspected of containing malicious information. The identification process is termed the steganalysis problem which focuses on both blind identification, in which only normal images are available for training, and multi-class identification, in which both the clean and stego images at several embedding rates are available for training. In this paper an investigation of a clustering and classification technique (Expectation Maximization with mixture models) is used to determine if a digital image contains hidden information. The steganalysis problem is for both anomaly detection and multi-class detection. The various clusters represent clean images and stego images with between 1% and 10% embedding percentage. Based on the results it is concluded that the EM classification technique is highly suitable for both blind detection and the multi-class problem.

Paper Details

Date Published: 30 April 2007
PDF: 9 pages
Proc. SPIE 6575, Visual Information Processing XVI, 657506 (30 April 2007); doi: 10.1117/12.720794
Show Author Affiliations
Benjamin M. Rodriguez, Air Force Institute of Technology (United States)
Gilbert L. Peterson, Air Force Institute of Technology (United States)
Sos S. Agaian, The Univ. of Texas at San Antonio (United States)

Published in SPIE Proceedings Vol. 6575:
Visual Information Processing XVI
Zia-ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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