Quantitative and Binary Steganalysis in JPEG: A Comparative Study

Ahmad Zakaria 1 Marc Chaumont 1 Gérard Subsol 1
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : We consider the problem of steganalysis, in which Eve (the steganalyst) aims to identify a steganogra-pher, Alice who sends images through a network. We can also hypothesise that Eve does not know how many bits Alice embed in an image. In this paper, we investigate two different steganalysis scenarios: Binary Steganalysis and Quantitative Steganalysis. We compare two classical steganalysis algorithms from the state-of-the-art: the QS algorithm and the GLRT-Ensemble Classifier, with features extracted from JPEG images obtained from BOSSbase 1.01. As their outputs are different, we propose a methodology to compare them. Numerical results with a state-of-the-art Content Adaptive Embedding Scheme and a Rich Model show that the approach of the GLRT-ensemble is better than the QS approach when doing Binary Steganalysis but worse when doing Quantitative Steganalysis.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01884006
Contributor : Marc Chaumont <>
Submitted on : Saturday, September 29, 2018 - 12:54:03 PM
Last modification on : Thursday, July 25, 2019 - 8:58:01 PM
Long-term archiving on : Sunday, December 30, 2018 - 12:20:29 PM

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Ahmad Zakaria, Marc Chaumont, Gérard Subsol. Quantitative and Binary Steganalysis in JPEG: A Comparative Study. EUSIPCO: European Signal Processing Conference, Sep 2018, Rome, Italy. pp.1422-1426. ⟨lirmm-01884006⟩

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