A Feature Selection Methodology for Steganalysis

Steganography has been known and used for a very long time, as a way to exchange information in an unnoticeable manner between parties, by embedding it in another, apparently innocuous, document. Nowadays steganographic techniques are mostly used on digital content. The online newspaper Wired News, reported in one of its articles [2] on steganography that several steganographic contents have been found on web sites with very large image database such as eBay. Niels Provos [3] has somewhat refuted these ideas by analyzing and classifying two million images from eBay and one million from USENet network and not finding any steganographic content embedded in these images. This could be due to many reasons, such as very low payloads, making the steganographic images very robust and secure to steganalysis. The security of a steganographic scheme has been defined theoretically by Cachin in [1] but this definition is very seldomly usable in practice. It requires to evaluate distributions and measure the Kullback-Leibler divergence between them. In practice, steganalysis is used as a way to evaluate the security of a steganographic scheme empirically: it aims at detecting whether a medium has been tampered with – but not to detect what is in the medium or how it has been embedded. By the use of features, one can get some relevant characteristics of the considered medium, and assess, by the use of machine learning tools, usually, whether the medium is genuine or not. This is only one way to perform steganalysis, but it remains the most common....Le principe de la stéganalyse est de classer un document incriminé comme original ou comme stéganographié. Cet article propose une méthodologie pour la stéganalyse utilisant la sélection de caractéristiques, orientée vers une diminution des intervales de confiance des résultats habituellement donnés. La sélection de caractéristiques permet également d’envisager une interprétation des caractéristiques d’images sélectionnées, dans le but de comprendre le fonctionnement intrinsèque des algorithmes de stéganographie. Il est montré que l’écart type des résultats obtenus habituellement en classification peut être très important (jusqu’à 5 %) lorsque des ensembles d’entrainements comportant trop peu d’échantillons sont utilisés. Ces tests sont menés sur six algorithmes de stéganographie, utilisés avec quatre taux d’insertions différents : 5, 10, 15 et 20 %. D’autre part, les caractéristiques sélectionnées (généralement 10 à 13 fois moins nombreuses que dans l’ensemble complet) permettent effectivement de faire ressortir les faiblesses ainsi que les avantages des algorithmes utilisés

Selection of robust features for the Cover Source Mismatch problem in 3D steganalysis

This paper introduces a novel method for extracting sets of feature from 3D objects characterising a robust stegan- alyzer. Specifically, the proposed steganalyzer should mitigate the Cover Source Mismatch (CSM) paradigm. A steganalyzer is considered as a classifier aiming to identify separately cover and stego objects. A steganalyzer behaves as a classifier by considering a set of features extracted from cover stego pairs of 3D objects as inputs during the training stage. However, during the testing stage, the steganalyzer would have to identify whether specific information was hidden in a set of 3D objects which can be different from those used during the training. Addressing the CSM paradigm corresponds to testing the generalization ability of the steganalyzer when introducing distortions in the cover objects before hiding information through steganography. Our method aims to select those 3D features that model best the changes introduced in objects by steganography or information hiding and moreover they are able to generalize for different objects, not present in the training set. The proposed robust steganalysis approach is tested when considering changes in 3D objects such as those produced by mesh simplification and additive noise. The results obtained from this study show that the steganalyzers trained with the selected set of robust features achieve better detection accuracy of the changes embedded in the objects, when compared to other sets of features

Efficient Parallel Feature Selection for Steganography Problems

The steganography problem consists of the identification of images hiding a secret message, which cannot be seen by visual inspection. This problem is nowadays becoming more and more important since the World Wide Web contains a large amount of images, which may be carrying a secret message. Therefore, the task is to design a classifier, which is able to separate the genuine images from the non-genuine ones. However, the main obstacle is that there is a large number of variables extracted from each image and the high dimensionality makes the feature selection mandatory in order to design an accurate classifier. This paper presents a new efficient parallel feature selection algorithm based on the Forward-Backward Selection algorithm. The results will show how the parallel implementation allows to obtain better subsets of features that allow the classifiers to be more accurate.TIN2007-60587, P07-TIC-02768 and P07-TIC-02906,TIC-3928Nokia Foundation, Finlan