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Daniel Cooper

dblp:194/8824 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
2 papers
Blockchain and cryptocurrency security · 28% Authentication and access control · 26% Privacy and data protection · 26%
Computer graphics and multimedia
1 paper
Multimedia systems and quality of experience · 100%

Topics — the 2 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Authentication and access control › identity management
digital identity
0.612022
TAPESTRY: A De-Centralized Service for Trusted Interaction Online · IEEE Trans. Serv. Comput. 2022
Digital forensics and information hiding › content authentication
video integrity verification
0.412020
Tamper-Proofing Video With Hierarchical Attention Autoencoder Hashing on Blockchain · IEEE Trans. Multim. 2020

Methods — techniques the papers use, named apart from their topics

self-supervised learning · 0.9proof-of-authority blockchain · 0.9hierarchical attention autoencoder · 0.9hybrid on-chain and off-chain storage · 0.6deep learning · 0.6
YearPublicationVenuePosition
2022 TAPESTRY: A De-Centralized Service for Trusted Interaction Online
abstract
We present a novel de-centralised service for proving the provenance of online digital identity, exposed as an assistive tool to help non-expert users make better decisions about whom to trust online. Our service harnesses the digital personhood (DP); the longitudinal and multi-modal signals created through users’ lifelong digital interactions, as a basis for evidencing the provenance of identity. We describe how users may exchange trust evidence derived from their DP, in a granular and privacy-preserving manner, with other users in order to demonstrate coherence and longevity in their behaviour online. This is enabled through a novel secure infrastructure combining hybrid on- and off-chain storage combined with deep learning for DP analytics and visualization. We show how our tools enable users to make more effective decisions on whether to trust unknown third parties online, and also to spot behavioural deviations in their own social media footprints indicative of account hijacking.
Daniel Cooper, John P. Collomosse, Constantin Catalin Dragan, Mark Manulis, Jamie Steane, Arthi Kanchana Manohar, Jo Briggs, Helen S. Jones, Wendy Moncur
IEEE Trans. Serv. Comput.2
2020 Tamper-Proofing Video With Hierarchical Attention Autoencoder Hashing on Blockchain
abstract
We present ARCHANGEL; a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we introduce a novel deep network architecture using a hierarchical attention autoencoder (HAAE) to compute temporal content hashes (TCHs) from minutes or hour-long audio-visual streams. Our TCHs are sensitive to accidental or malicious content modification (tampering). The focus of our self-supervised HAAE is to guard against content modification such as frame truncation or corruption but ensure invariance against format shift (i.e. codec change). This is necessary due to the curatorial requirement for archives to format shift video over time to ensure future accessibility. Second, we describe how the TCHs (and the models used to derive them) are secured via a proof-of-authority blockchain distributed across multiple independent archives. We report on the efficacy of ARCHANGEL within the context of a trial deployment in which the national government archives of the United Kingdom, United States of America, Estonia, Australia and Norway participated.
Tu Bui, Daniel Cooper, John P. Collomosse, Mark Bell, Alex Green 0002, John Sheridan, Jez Higgins, Arindra Das, Jared Keller 0001, Olivier Thereaux
IEEE Trans. Multim.2