HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadata

https://hal-lirmm.ccsd.cnrs.fr/lirmm-01884006
Contributor : Marc Chaumont Connect in order to contact the contributor
Submitted on : Saturday, September 29, 2018 - 12:54:03 PM
Last modification on : Tuesday, March 15, 2022 - 12:55:41 PM
Long-term archiving on: : Sunday, December 30, 2018 - 12:20:29 PM

File

EUSIPCO-2018_ZAKARIA_CHAUMONT_...
Files produced by the author(s)

Identifiers

  • HAL Id : lirmm-01884006, version 1

Citation

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⟩

Share

Metrics

Record views

95

Files downloads

177