Reliable Steganalysis Using a Minimum Set of Samples and Features

Yoan Miche 1 Patrick Bas 2 Christian Jutten 3 Amaury Lendasse 1 Olli Simula 1
2 GIPSA-C2S - C2S
GIPSA-DIS - Département Images et Signal
3 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal, GIPSA-PSD - GIPSA Pôle Sciences des Données
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 classification 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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Yoan Miche, Patrick Bas, Christian Jutten, Amaury Lendasse, Olli Simula. Reliable Steganalysis Using a Minimum Set of Samples and Features. EURASIP Journal on Information Security, Hindawi, 2009, 2009, pp.ID 901381. ⟨hal-00437868⟩

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