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PHASER: Perceptual Hashing Algorithms Evaluation and Results -an Open Source Forensic Framework

Mckeown, Sean; Aaby, Peter; Steyven, Andreas



The automated comparison of visual content is a contemporary solution to scale the detection of illegal media and extremist material, both for detection on individual devices and in the cloud. However, the problem is difficult, and perceptual similarity algorithms often have weaknesses and anomalous edge cases that may not be clearly documented. Additionally, it is a complex task to perform an evaluation of such tools in order to best utilise them. To address this, we present PHASER, a still-image perceptual hashing framework enabling forensics specialists and scientists to conduct experiments on bespoke datasets for their individual deployment scenarios. The framework utilises a modular approach, allowing users to specify and define a perceptual hash/image transform/ distance metric triplet, which can be explored to better understand their behaviour and interactions. PHASER is open-source and we demonstrate its utility via case studies which briefly explore setting an appropriate dataset size and the potential to optimise the performance of existing algorithms by utilising learned weight vectors for comparing hashes.

Presentation Conference Type Conference Paper (published)
Conference Name DFRWS EU 2024
Acceptance Date Nov 27, 2023
Online Publication Date Mar 15, 2024
Publication Date 2024-03
Deposit Date Mar 1, 2024
Publicly Available Date Mar 16, 2025
Journal Forensic Science International: Digital Investigation
Electronic ISSN 2666-2817
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 48
Issue Supplement
Article Number 301680
Series ISSN 2666-2817
Keywords Evaluation Framework; Perceptual Hashing; Hashing; Content Matching; Image Forensics
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