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) allowed obtaining steganalysis results by "deep learning" who are approaching the results of two-step approaches (EC + RM). Therefore, since 2015, 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, in stegananalysis with images of arbitrary size, etc.

In this presentation, we will discuss the infancy of CNNs in steganography / steganalysis. We will recall the steganography / steganalysis purposes, and the generic structure of modern convolutional neural networks. We will present the best networks proposed in 2018 used for spatial steganalysis. Finally, we will present the four families of steganography by GAN, and discuss the perspectives of the field.