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Generative Adversarial Networks for Image Steganography
Denis Volkhonskiy, Boris Borisenko, Evgeny Burnaev
Nov 04, 2016 (modified: Nov 05, 2016)ICLR 2017 conference submissionreaders: everyone
Abstract:Steganography is collection of methods to hide secret information ("payload") within non-secret information ("container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.
TL;DR:We consider a new type of GAN model and apply it to secure image steganography
Conflicts:hse.ru, iitp.ru, skoltech.ru
Keywords:Computer vision, Deep learning, Unsupervised Learning, Applications, Supervised Learning
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