Security and forensics exploration of learning-based image coding

Bhowmik, Deepayan and Elawady, Mohamed and Nogueira, Keiller; (2022) Security and forensics exploration of learning-based image coding. In: 2021 International Conference on Visual Communications and Image Processing (VCIP). IEEE International Conference on Visual Communications and Image Processing (VCIP) . IEEE, Piscataway, NJ, pp. 1-5. ISBN 9781728185514 (https://doi.org/10.1109/VCIP53242.2021.9675445)

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Abstract

Advances in media compression indicate significant potential to drive future media coding standards, e.g., Joint Photographic Experts Group's learning-based image coding technologies (JPEG AI) and Joint Video Experts Team's (JVET) deep neural networks (DNN) based video coding. These codecs in fact represent a new type of media format. As a dire consequence, traditional media security and forensic techniques will no longer be of use. This paper proposes an initial study on the effectiveness of traditional watermarking on two state-of-the-art learning based image coding. Results indicate that traditional watermarking methods are no longer effective. We also examine the forensic trails of various DNN architectures in the learning based codecs by proposing a residual noise based source identification algorithm that achieved 79% accuracy.

  • Item type:Book Section
    ID code:84570
    Dates:
    Date
    Event
    19 January 2022
    Published
    8 December 2021
    Published Online
    27 June 2021
    Accepted
    Notes:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:UNSPECIFIED
    Depositing user: Pure Administrator
    Date deposited:07 Mar 2023 10:22
    Last modified:22 Apr 2024 00:10
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/84570
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