Deep Learning in steganography and steganalysis from 2015 to 2018
This work addresses the problem of detecting hidden messages in images for security and forensics applications, but it is incremental as it reviews and synthesizes existing advancements rather than introducing new methods.
The paper reviews deep learning methods in steganography and steganalysis from 2015 to 2018, showing that convolutional neural networks (CNNs) have achieved improved performances in various steganalysis scenarios, approaching or surpassing traditional methods like Rich Models and Ensemble Classifiers.
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.