PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Reliable Steganalysis Using a Minimum Set of Samples and Features
Yoan Miche, Patrick Bas, Amaury Lendasse, Christian Jutten and Olli Simula
EURASIP Journal on Information Security 2009.

Abstract

This paper proposes to determine a sufficient number of images for reliable classification, and to use feature selection to select most relevant features for achieving reliable steganalysis. First dimensionality issues in the context of classification are outlined and the impact of the different parameters of a steganalysis scheme (the number of samples, the number of features, the steganography method and the embedding rate) is studied. On one hand, it is shown that using Bootstrap simulations, the standard deviation of the classifications results can be very important if too small training sets are used; moreover a minimum of 5000 images is needed in order to perform reliable steganalysis. On the other hand, we show how the feature selection process using the OP-ELM classifier enables both to reduce the dimensionality of the data and to highlight weaknesses and advantages of the six most popular steganographic algorithms.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:4919
Deposited By:Amaury Lendasse
Deposited On:24 March 2009