TRAIT: a trusted media distribution framework

James Rainey; Mohamed Elawady; Charith Abhayartne; Deepayan Bhowmik

doi:10.1109/DSP58604.2023.10167909

TRAIT: a trusted media distribution framework

James Rainey, Mohamed Elawady, Charith Abhayartne, Deepayan Bhowmik

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

1 Citation (Scopus)

38 Downloads (Pure)

Abstract

Trusted distribution and consumption of media content has become a challenging issue, especially with the advancement of machine learning-based techniques such as deep fake. To address such challenges, this paper proposes a new metadata schema which is embedded within a larger framework that facilitates trusted media distribution. This schema is realised through a distributed media blockchain core in conjunction with algorithms to detect media modifications. Such a framework is expected to improve trust in media consumption, ensuring media integrity, authenticity and provenance.

Original language	English
Title of host publication	2023 24th International Conference on Digital Signal Processing (DSP)
Place of Publication	Piscataway, N.J.
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	9798350339598
DOIs	https://doi.org/10.1109/DSP58604.2023.10167909
Publication status	Published - 5 Jul 2023
Event	2023 24th International Conference on Digital Signal Processing (DSP) - Rhodes (Rodos), Greece Duration: 11 Jul 2023 → 13 Jul 2023 https://2023.ic-dsp.org/

Conference

Conference	2023 24th International Conference on Digital Signal Processing (DSP)
Country/Territory	Greece
City	Rhodes (Rodos)
Period	11/07/23 → 13/07/23
Internet address	https://2023.ic-dsp.org/

Keywords

machine learning algorithm
signal processing algorithms
blockchains

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10.1109/DSP58604.2023.10167909

Rainey-etal-ICDSP-2023-TRAIT-a-trusted-media-distribution-frameworkAccepted author manuscript, 1.32 MBLicence: Strath_1

Cite this

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title = "TRAIT: a trusted media distribution framework",

abstract = "Trusted distribution and consumption of media content has become a challenging issue, especially with the advancement of machine learning-based techniques such as deep fake. To address such challenges, this paper proposes a new metadata schema which is embedded within a larger framework that facilitates trusted media distribution. This schema is realised through a distributed media blockchain core in conjunction with algorithms to detect media modifications. Such a framework is expected to improve trust in media consumption, ensuring media integrity, authenticity and provenance.",

keywords = "machine learning algorithm, signal processing algorithms, blockchains",

author = "James Rainey and Mohamed Elawady and Charith Abhayartne and Deepayan Bhowmik",

note = "{\textcopyright} 2023 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.; 2023 24th International Conference on Digital Signal Processing (DSP) ; Conference date: 11-07-2023 Through 13-07-2023",

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AU - Rainey, James

AU - Elawady, Mohamed

AU - Abhayartne, Charith

AU - Bhowmik, Deepayan

N1 - © 2023 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.

PY - 2023/7/5

Y1 - 2023/7/5

N2 - Trusted distribution and consumption of media content has become a challenging issue, especially with the advancement of machine learning-based techniques such as deep fake. To address such challenges, this paper proposes a new metadata schema which is embedded within a larger framework that facilitates trusted media distribution. This schema is realised through a distributed media blockchain core in conjunction with algorithms to detect media modifications. Such a framework is expected to improve trust in media consumption, ensuring media integrity, authenticity and provenance.

AB - Trusted distribution and consumption of media content has become a challenging issue, especially with the advancement of machine learning-based techniques such as deep fake. To address such challenges, this paper proposes a new metadata schema which is embedded within a larger framework that facilitates trusted media distribution. This schema is realised through a distributed media blockchain core in conjunction with algorithms to detect media modifications. Such a framework is expected to improve trust in media consumption, ensuring media integrity, authenticity and provenance.

KW - machine learning algorithm

KW - signal processing algorithms

KW - blockchains

U2 - 10.1109/DSP58604.2023.10167909

DO - 10.1109/DSP58604.2023.10167909

M3 - Conference contribution book

BT - 2023 24th International Conference on Digital Signal Processing (DSP)

PB - IEEE

CY - Piscataway, N.J.

Y2 - 11 July 2023 through 13 July 2023

ER -

TRAIT: a trusted media distribution framework

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