![]() ![]() Since 2014, researchers have used Convolutional Neural Networks to solve this problem generating diverse architectures and strategies to improve the detection percentages of steganographic images on the last generation algorithms (WOW, S-UNIWARD, HUGO, J-UNIWARD, among others). The results of these techniques have surpassed those obtained with conventional methods -Rich Models with Ensemble Classifiers -both in the spatial and frequency (JPEG) domains. ![]() This paper shows the evolution of steganalysis in recent years using Deep Learning techniques. In recent years, the development of Deep Learning has made it possible to unify and automate the two traditional stages into an end to end approach with promising results. OUTGUESS HIDE TEXT IN PHOTOS MANUALTraditionally, steganalysis has been divided into two separate stages, the first stage consists of manual extraction of sophisticated features and the second stage is classification using Ensemble Classifiers or Support Vector Machines. Steganalysis is dedicated to the detection of hidden messages using steganography these messages can be implicit in different types of media, such as digital images, video files, audio files or plain text. Steganography consists of hiding messages inside some object known as a carrier in order to establish a covert communication channel so that the act of communication itself goes unnoticed by observers who have access to that channel. So we will present the structure of a deep neural network in a generic way and present the networks proposed in the existing literature for different scenarios of steganalysis, and finally, we will discuss steganography by deep learning. We do not intend to repeat the basic concepts of machine learning or deep learning. 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, evaluated with a methodology specific to the discipline of steganalysis. During 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 quantitative steganalysis. 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). ![]() Thus, this work has wide and secure applications in many fields.įor almost 10 years, the detection of a hidden message in an image has been mainly carried out by the computation of rich models (RMs), followed by classification using an ensemble classifier (EC). ![]() The network is only trained once, irrespective of the different container images and secret messages given as inputs. Also, other steganography softwares cannot be used to reveal the message since the model is trained using a deep learning algorithm which complicates its steganalysis. The main aspect of this work is it produces minimal distortion to the secret message. The decoder network on the receiving side, which is simultaneously trained with the encoder, reveals the secret image. OUTGUESS HIDE TEXT IN PHOTOS HOW TOThe encoder neural network determines where and how to place the message, dispersing it throughout the bits of cover image. In this project, along with LSB encoding, deep learning modules using the Adam algorithm are used to train the model that comprises a hiding network and a reveal network. The cover image also called the carrier can be publicly visible. An N * N RGB pixel secret message (either text or image) is to be transmitted in another N * N RGB container image with minimum changes in its contents. The goal in our project is to hide digital messages using modern steganography techniques. Steganography has been used since centuries for concealment of messages in a cover media where messages were physically hidden. ![]()
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