Outguess software download5/28/2023 Au cours des dernières années, plusieurs études ont montré que les réseaux de neurones convolutionnels peuvent atteindre des performances supérieures à celles des approches conventionnelles d'apprentissage machine.Le sujet de cette thèse traite des techniques d'apprentissage profond utilisées pour la stéganographie d'images et la stéganalyse dans le domaine spatial.La première contribution est un réseau de neurones convolutionnel rapide et efficace pour la stéganalyse, nommé Yedroudj-Net. Pendant une dizaine d'années, l'approche classique en stéganalyse a été d'utiliser un ensemble classifieur alimenté par des caractéristiques extraites "à la main". La stéganalyse d'image a elle pour objectif de détecter la présence d'un message caché en recherchant les artefacts présent dans l'image. La stéganographie d'image est l'art de la communication secrète dans le but d'échanger un message de manière furtive. For the steganalyzer we use Yedroudj-Net, this for its affordable size, and for the fact that its training does not require the use of any tricks that could increase the computational time.This second contribution defines a research direction, by giving first reflection elements while giving promising first results. The training of this steganographic system is conducted using three neural networks that compete against each other: the embedder, the extractor, and the steganalyzer. Our proposed steganography system is based on the use of generative adversarial networks. Among the existing techniques, we focus on the 3-player game approach.We propose an embedding algorithm that automatically learns how to hide a message secretly. Among these add-ons, we have evaluated the data augmentation, and the the use of an ensemble of CNN Both increase our CNN performances.The second contribution is the application of deep learning techniques for steganography i.e the embedding. Moreover,Yedroudj-Net can easily be improved by using well known add-ons. Compared tomodern deep learning based steganalysis methods, Yedroudj-Net can achieve state-of-the-art detection results, but also takes less time to converge, allowing the use of a large training set. In recent years, studies have shown that well-designed convolutional neural networks (CNNs) can achieve superior performance compared to conventional machine-learning approaches.The subject of this thesis deals with the use of deep learning techniques for image steganography and steganalysis in the spatialdomain.The first contribution is a fast and very effective convolutional neural network for steganalysis, named Yedroudj-Net. For about ten years, the classic approach for steganalysis was to use an Ensemble Classifier fed by hand-crafted features. In the other hand, image steganalysis attempts to detect the presence of a hidden message by searching artefacts within an image. It has been tested on a variety of Unix-like operating systems and is included in the standard software repositories of the popular Linux distributions Debian and Arch Linux (via user repository) and their derivatives.Image steganography is the art of secret communication in order to exchange a secret message. It is written in C and published as Free Software under the terms of the old BSD license. It has handlers for image files in the common Netpbm and JPEG formats, so it can, for example, specifically alter the frequency coefficients of JPEG files. OutGuess is a steganographic software for hiding data in the most redundant content data bits of existing (media) files.It has been tested on a variety of Unix-like operating systems and is included in the standard software repositories of the popular Linux distributions Debian and Arch Linux (via user repository) and their derivatives.
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