Outguess steganography windows how to#
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. Image steganography is the art of secret communication in order to exchange a secret message.
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