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Explicit Optimization of min max Steganographic Game

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00348527" target="_blank" >RIV/68407700:21230/21:00348527 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/TIFS.2020.3021913" target="_blank" >https://doi.org/10.1109/TIFS.2020.3021913</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/TIFS.2020.3021913" target="_blank" >10.1109/TIFS.2020.3021913</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Explicit Optimization of min max Steganographic Game

  • Original language description

    This article proposes an algorithm which allows Alice to simulate the game played between her and Eve. Under the condition that the set of detectors that Alice assumes Eve to have is sufficiently rich (e.g. CNNs), and that she has an algorithm enabling to avoid detection by a single classifier (e.g adversarial embedding, gibbs sampler, dynamic STCs), the proposed algorithm converges to an efficient steganographic algorithm. This is possible by using a min max strategy which consists at each iteration in selecting the least detectable stego image for the best classifier among the set of Eve's learned classifiers. The algorithm is extensively evaluated and compared to prior arts and results show the potential to increase the practical security of classical steganographic methods. For example the error probability P-err of XU-Net on detecting stego images with payload of 0.4 bpnzAC embedded by J-Uniward and QF 75 starts at 7.1% and is increased by +13.6% to reach 20.7% after eight iterations. For the same embedding rate and for QF 95, undetectability by XU-Net with J-Uniward embedding is 23.4%, and it jumps by +25.8% to reach 49.2% at iteration 3.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    IEEE Transactions on Information Forensics and Security

  • ISSN

    1556-6013

  • e-ISSN

    1556-6021

  • Volume of the periodical

    2020

  • Issue of the periodical within the volume

    16

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    12

  • Pages from-to

    812-823

  • UT code for WoS article

    000576264500009

  • EID of the result in the Scopus database

    2-s2.0-85092464606