  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, 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.
