Plug to Place: Indoor Multimedia Geolocation from Electrical Sockets for Digital Investigation
Kanwal Aftab, Graham Adams, Mark Scanlon

TL;DR
This paper presents a novel indoor geolocation method using electrical socket types as markers, leveraging deep learning to classify socket types and map them to locations, aiding digital investigations in challenging environments.
Contribution
Introduces a deep learning pipeline for indoor geolocation using socket types as consistent markers, with new datasets and real-world evaluation for forensic applications.
Findings
High accuracy in socket detection ([email protected]=0.843)
Socket classification accuracy of 91.2%
Country mapping accuracy of 96%
Abstract
Computer vision is a rapidly evolving field, giving rise to powerful new tools and techniques in digital forensic investigation, and shows great promise for novel digital forensic applications. One such application, indoor multimedia geolocation, has the potential to become a crucial aid for law enforcement in the fight against human trafficking, child exploitation, and other serious crimes. While outdoor multimedia geolocation has been widely explored, its indoor counterpart remains underdeveloped due to challenges such as similar room layouts, frequent renovations, visual ambiguity, indoor lighting variability, unreliable GPS signals, and limited datasets in sensitive domains. This paper introduces a pipeline that uses electric sockets as consistent indoor markers for geolocation, since plug socket types are standardised by country or region. The three-stage deep learning pipeline…
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Taxonomy
TopicsDigital Media Forensic Detection · Digital and Cyber Forensics · Autopsy Techniques and Outcomes
