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How to augment a small learning set for improving the performances of a CNN-based steganalyzer?

Mehdi Yedroudj 1 Marc Chaumont 1, 2 Frédéric Comby 1, 2 
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Deep learning and convolutional neural networks (CNN) have been intensively used in many image processing topics during last years. As far as steganalysis is concerned, the use of CNN allows reaching the state-of-the-art results. The performances of such networks often rely on the size of their learning database. An obvious preliminary assumption could be considering that the bigger a database is, the better the results are. However, it appears that cautions have to be taken when increasing the database size if one desire to improve the classification accuracy i.e. enhance the steganalysis efficiency. To our knowledge, no study has been performed on the enrichment impact of a learning database on the steganalysis performance. What kind of images can be added to the initial learning set? What are the sensitive criteria: the camera models used for acquiring the images, the treatments applied to the images, the cameras proportions in the database, etc? This article continues the work carried out in a previous paper in submission [1], and explores the ways to improve the performances of CNN. It aims at studying the effects of base augmentation on the performance of steganalysis using a CNN. We present the results of this study using various experimental protocols and various databases to define the good practices in base augmentation for steganalysis.
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Submitted on : Saturday, February 3, 2018 - 3:57:00 PM
Last modification on : Friday, August 5, 2022 - 3:02:19 PM
Long-term archiving on: : Friday, May 25, 2018 - 7:21:57 PM


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Mehdi Yedroudj, Marc Chaumont, Frédéric Comby. How to augment a small learning set for improving the performances of a CNN-based steganalyzer?. Electronic Imaging, ingenta CONNECT, 2018, Media Watermarking, Security, and Forensics 2018, 317, pp.317-1-317-7. ⟨10.2352/ISSN.2470-1173.2018.07.MWSF-317⟩. ⟨lirmm-01681883v2⟩



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