Steganographic generative adversarial networks

Denis Volkhonskiy, Ivan Nazarov, Evgeny Burnaev

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    28 Citations (Scopus)

    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.

    Original languageEnglish
    Title of host publication12th International Conference on Machine Vision, ICMV 2019
    EditorsWolfgang Osten, Dmitry Nikolaev, Jianhong Zhou
    PublisherSPIE
    ISBN (Electronic)9781510636439
    DOIs
    Publication statusPublished - 2020
    Event12th International Conference on Machine Vision, ICMV 2019 - Amsterdam, Netherlands
    Duration: 16 Nov 201918 Nov 2019

    Publication series

    NameProceedings of SPIE - The International Society for Optical Engineering
    Volume11433
    ISSN (Print)0277-786X
    ISSN (Electronic)1996-756X

    Conference

    Conference12th International Conference on Machine Vision, ICMV 2019
    Country/TerritoryNetherlands
    CityAmsterdam
    Period16/11/1918/11/19

    Keywords

    • Generative adversarial networks
    • Security
    • Steganography

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