For almost 10 years, the detection of a message hidden in an image has been mainly carried out by the computation of a Rich Model (RM), followed by a classification by 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 results of two-step approaches (EC + RM). Therefore, over the 2015-2018 period, numerous publications have shown that it is possible to obtain better performances notably in spatial steganalysis, in JPEG steganalysis, in Selection-Channel-Aware steganalysis, in quantitative steganalysis. This chapter deals with deep learning in steganalysis from the point of view of the existing, by presenting the different neural networks that have been evaluated with a methodology specific to the discipline of steganalysis, and this during the period 2015-2018. The chapter is not intended to repeat the basic concepts of machine learning or deep learning. We will thus give in a generic way the structure of a deep neural network, we will present the networks proposed in the literature for the different scenarios of steganalysis, and finally, we will discuss steganography by GAN.