Steganalysis with cover-source mismatch and a small learning database

Jérôme Pasquet (1) , Sandra Bringay (2, 3) , Marc Chaumont (1)

1 ICAR - Image & Interaction
2 ADVANSE - ADVanced Analytics for data SciencE
3 UPVM - Université Paul-Valéry - Montpellier 3

Many different hypotheses may be chosen for modeling a steganography/steganalysis problem. In this paper, we look closer into the case in which Eve, the steganalyst, has partial or erroneous knowledge of the cover distribution. More precisely we suppose that Eve knows the algorithms and the payload size that has been used by Alice, the steganographer, but she ignores the images distribution. In this source-cover mismatch scenario, we demonstrate that an Ensemble Classifier with Features Selection (EC-FS) allows the steganalyst to obtain the best state-of-the-art performances, while requiring 100 times smaller training database compared to the previous state-of-the art approach. Moreover, we propose the islet approach in order to increase the classification performances.

Mots clés: Steganalysis; Cover-Source Mismatch; Ensemble Classifiers with Post-Selection of Features; Ensemble Average Perceptron; Clustering

Domaines: Cryptographie et sécurité [cs.CR]; Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]

Jérôme Pasquet, Sandra Bringay, Marc Chaumont. Steganalysis with cover-source mismatch and a small learning database. EUSIPCO: European Signal Processing Conference, Sep 2014, Lisbon, Portugal. pp.2425-2429. ⟨lirmm-01234249⟩