Generative Adversarial Networks for Image Steganography
Denis Volkhonskiy, Б. Б. Борисенко, Evgeny Vladimirovich Burnaev · 2017
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.