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.
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