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Proceedings Paper

Improved stego sensitivity measure for ± A steganalysis
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Paper Abstract

This article addresses four basic goals; 1) the evaluation of sequentially and randomly embedded stego evidence within digital images 2) the identification of "steganographic fingerprint" for special domain based steganographic methods, 3) the reduction of steganalysis false detection rate, and 4) the investigation of two well known pixel comparison based steganalysis methods. We present an improved version of Stego Sensitivity Measure, which is based on the statistics of sample pair (the basic unit), rather than individual samples which is very sensitive to ± A embedding. The presented measure enhances stego detection accuracy and localization of stego areas within sequentially and randomly embedded color or gray scale stego images. In addition, it estimates the message length of an embedded bit-stream within bit planes of a digital image, and it has better localization of steganography detected along with an improved estimation of the message length. It also identifies the "steganographic fingerprint" of special domain sequentially and randomly based steganographic methods. Numerical experimentation was conducted with an arbitrary image database of 200 color TIFF and RAW images taken with the Nikon D100 and the Canon EOS Digital Rebel cameras. In this article comparison are also shown using two known steganalysis methods Raw Quick Pairs and RS Steganalysis which have revealed that; a) The false alarm rate for the proposed detection method is p = 0.9 for a database of 200 images clean images while RS Steganalysis has shown a high false alarm rate for clean images of p = 2.8. b) The two methods Raw Quick Pairs and RS Steganalysis cannot be used for localization of steganographic regions due to the statistical properties of the detection methods.

Paper Details

Date Published: 12 May 2006
PDF: 11 pages
Proc. SPIE 6246, Visual Information Processing XV, 62460C (12 May 2006); doi: 10.1117/12.665883
Show Author Affiliations
Sos S. Agaian, The Univ. of Texas at San Antonio (United States)
Benjamin M. Rodriguez, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 6246:
Visual Information Processing XV
Zia-ur Rahman; Stephen E. Reichenbach; Mark A. Neifeld, Editor(s)

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