Steganographic Generative Adversarial Networks

2017/03/16 by Volkhonskiy, Denis, Nazarov, Ivan, Burnaev, Evgeny · 1 citation
#Applications (stat.AP) #Computer Vision and Pattern Recognition (cs.CV) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Multimedia (cs.MM)

paper · doi:10.48550/arxiv.1703.05502

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.

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• StegOT: Trade-offs in Steganography via Optimal Transport

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