Steganographic Generative Adversarial Networks

Autor:	Volkhonskiy, Denis, Nazarov, Ivan, Burnaev, Evgeny
Rok vydání:	2017
Předmět:	Computer Science - Multimedia Computer Science - Cryptography and Security Computer Science - Computer Vision and Pattern Recognition Statistics - Applications
Druh dokumentu:	Working Paper
Popis:	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. Comment: 15 pages, 10 figures, 5 tables, Workshop on Adversarial Training (NIPS 2016, Barcelona, Spain)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1703.05502 Zobrazit plný text záznamu View this record from Arxiv