Steganographic generative adversarial networks

Denis Volkhonskiy; Ivan Nazarov; Evgeny Burnaev

doi:10.1117/12.2559429

31 January 2020 Steganographic generative adversarial networks

Denis Volkhonskiy, Ivan Nazarov, Evgeny Burnaev

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114333M (2020) https://doi.org/10.1117/12.2559429
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

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.

Citation Download Citation

Denis Volkhonskiy, Ivan Nazarov, and Evgeny Burnaev "Steganographic generative adversarial networks", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114333M (31 January 2020); https://doi.org/10.1117/12.2559429

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