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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Bhowmik, Deepayan
Elawady, Mohamed
Nogueira, Keiller
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Title: Security and Forensics Exploration of Learning-based Image Coding
Citation: Bhowmik D, Elawady M & Nogueira K (2021) Security and Forensics Exploration of Learning-based Image Coding. In: 2021 IEEE International Conference on Visual Communications and Image Processing (VCIP). Visual Communications and Image Processing (VCIP 2021), Munich, 05.12.2021-08.12.2021. Piscataway, NJ, USA: IEEE.
Issue Date: 2021
Date Deposited: 26-Oct-2021
Conference Name: Visual Communications and Image Processing (VCIP 2021)
Conference Dates: 2021-12-05 - 2021-12-08
Conference Location: Munich
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 MJoint 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.
Status: AM - Accepted Manuscript
Rights: © 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.

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