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Proceedings Paper

Performance evaluation of blind steganalysis classifiers
Author(s): Mark T. Hogan; Guenole C. M. Silvestre; Neil J. Hurley
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Paper Abstract

Steganalysis is the art of detecting and/or decoding secret messages embedded in multimedia contents. The topic has received considerable attention in recent years due to the malicious use of multimedia documents for covert communication. Steganalysis algorithms can be classified as either blind or non-blind depending on whether or not the method assumes knowledge of the embedding algorithm. In general, blind methods involve the extraction of a feature vector that is sensitive to embedding and is subsequently used to train a classifier. This classifier can then be used to determine the presence of a stego-object, subject to an acceptable probability of false alarm. In this work, the performance of three classifiers, namely Fisher linear discriminant (FLD), neural network (NN) and support vector machines (SVM), is compared using a recently proposed feature extraction technique. It is shown that the NN and SVM classifiers exhibit similar performance exceeding that of the FLD. However, steganographers may be able to circumvent such steganalysis algorithms by preserving the statistical transparency of the feature vector at the embedding. This motivates the use of classification algorithms based on the entire document. Such a strategy is applied using SVM classification for DCT, FFT and DWT representations of an image. The performance is compared to a feature extraction technique.

Paper Details

Date Published: 22 June 2004
PDF: 12 pages
Proc. SPIE 5306, Security, Steganography, and Watermarking of Multimedia Contents VI, (22 June 2004); doi: 10.1117/12.526660
Show Author Affiliations
Mark T. Hogan, Univ. College Dublin (Ireland)
Guenole C. M. Silvestre, Univ. College Dublin (Ireland)
Neil J. Hurley, Univ. College Dublin (Ireland)

Published in SPIE Proceedings Vol. 5306:
Security, Steganography, and Watermarking of Multimedia Contents VI
Edward J. Delp III; Ping W. Wong, Editor(s)

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