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Computational and Ambient Intelligence
Steganalysis consists in classifying documents as steganographied or genuine. This paper presents a methodology for steganalysis based on a set of 193 features with two main goals: determine a sufficient number of images for effective training of a classifier in the obtained high-dimensional space, and use feature selection to select most relevant features for the desired classification. Dimensionality reduction is performed using a forward selection and reduces the original 193 features set by a factor of 13, with overall same performance.doi:10.1007/978-3-540-73007-1_73 dblp:conf/iwann/MicheBLJS07 fatcat:c2g2hmqutbhhzfbxjqkes6zv4u