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Proceedings Paper

Feature selection for steganalysis using the Mahalanobis distance
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Paper Abstract

Steganalysis is used to detect hidden content in innocuous images. Many successful steganalysis algorithms use a large number of features relative to the size of the training set and suffer from a "curse of dimensionality": large number of feature values relative to training data size. High dimensionality of the feature space can reduce classification accuracy, obscure important features for classification, and increase computational complexity. This paper presents a filter-type feature selection algorithm that selects reduced feature sets using the Mahalanobis distance measure, and develops classifiers from the sets. The experiment is applied to a well-known JPEG steganalyzer, and shows that using our approach, reduced-feature steganalyzers can be obtained that perform as well as the original steganalyzer. The steganalyzer is that of Pevn´y et al. (SPIE, 2007) that combines DCT-based feature values and calibrated Markov features. Five embedding algorithms are used. Our results demonstrate that as few as 10-60 features at various levels of embedding can be used to create a classifier that gives comparable results to the full suite of 274 features.

Paper Details

Date Published: 28 January 2010
PDF: 12 pages
Proc. SPIE 7541, Media Forensics and Security II, 754104 (28 January 2010); doi: 10.1117/12.841074
Show Author Affiliations
Jennifer L. Davidson, Iowa State Univ. (United States)
Jaikishan Jalan, Iowa State Univ. (United States)


Published in SPIE Proceedings Vol. 7541:
Media Forensics and Security II
Nasir D. Memon; Jana Dittmann; Adnan M. Alattar; Edward J. Delp, Editor(s)

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