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Security and Forensics Exploration of Learning-based Image Coding

Lookup NU author(s): Dr Deepayan BhowmikORCiD


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© 2021 IEEE. 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.

Publication metadata

Author(s): Bhowmik D, Elawady M, Nogueira K

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Conference on Visual Communications and Image Processing (VCIP 2021)

Year of Conference: 2021

Online publication date: 19/01/2022

Acceptance date: 02/04/2018

ISSN: 2642-9357

Publisher: IEEE


DOI: 10.1109/VCIP53242.2021.9675445

Library holdings: Search Newcastle University Library for this item

ISBN: 9781728185514