  For almost 10 years, the detection of a hidden message in an image has been
mainly carried out by the computation of Rich Models (RM), followed by
classification using an Ensemble Classifier (EC). In 2015, the first study
using a convolutional neural network (CNN) obtained the first results of
steganalysis by Deep Learning approaching the performances of the two-step
approach (EC + RM). Between 2015-2018, numerous publications have shown that it
is possible to obtain improved performances, notably in spatial steganalysis,
JPEG steganalysis, Selection-Channel-Aware steganalysis, and in quantitative
steganalysis. This chapter deals with deep learning in steganalysis from the
point of view of current methods, by presenting different neural networks from
the period 2015-2018, that have been evaluated with a methodology specific to
the discipline of steganalysis. The chapter is not intended to repeat the basic
concepts of machine learning or deep learning. So, we will present the
structure of a deep neural network, in a generic way and present the networks
proposed in existing literature for the different scenarios of steganalysis,
and finally, we will discuss steganography by deep learning.
