Bui Tu, Cooper Daniel, Collomosse John, Bell Mark, Green Alex, Sheridan John, Higgins Jez, Das Arindra, Keller Jared Robert, Thereaux Olivier (2020) Tamper-proofing Video with Hierarchical Attention
Autoencoder Hashing on Blockchain,IEEE Transactions on Multimedia
Institute of Electrical and Electronics Engineers
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 hourlong 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.
Yang Yifan, Cooper Daniel, Collomosse John, Dragan Catalin, Manulis Mark, Briggs Jo, Steane Jamie, Manohar Arthi, Moncur Wendy, Jones Helen (2020) TAPESTRY: A De-centralized Service for Trusted Interaction Online,IEEE Transactions on Services Computingpp. 1-1
Institute of Electrical and Electronics Engineers (IEEE)
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.