# Deep Learning in steganography and steganalysis from 2015 to 2018

## TL;DR

This paper reviews the evolution of deep learning techniques in steganalysis from 2015 to 2018, highlighting improvements over traditional methods and discussing neural network architectures used in the field.

## Contribution

It provides a comprehensive overview of neural network models applied to steganalysis during 2015-2018 and evaluates their performance with discipline-specific methodologies.

## Key findings

- Deep learning approaches achieved performance comparable to traditional methods.
- Neural networks improved spatial and JPEG steganalysis accuracy.
- Discussion on the potential of deep learning for steganography detection.

## Abstract

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.