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Details of Grant 

EPSRC Reference: EP/V008870/1
Title: TAPS: Assessing, Mitigating and Raising Awareness of the Security and Privacy Risks of Thermal Imaging
Principal Investigator: Khamis, Dr M
Other Investigators:
Researcher Co-Investigators:
Project Partners:
CENSIS Logitech (International)
Department: School of Computing Science
Organisation: University of Glasgow
Scheme: New Investigator Award
Starts: 01 February 2021 Ends: 31 January 2023 Value (£): 262,119
EPSRC Research Topic Classifications:
Artificial Intelligence Human-Computer Interactions
Image & Vision Computing
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
03 Aug 2020 EPSRC ICT Prioritisation Panel August 2020 Announced
Summary on Grant Application Form
Thermal imaging technologies are continuously becoming more affordable and accessible to everyone. Today, a thermal camera can be bought for less than £150. Thermal imaging can be used maliciously to infer the user input on keyboards and touchscreens. For example, taking a thermal image of a keyboard after a user has interacted with it reveals recent input such as passwords, or sensitive messages. This project aims to 1) assess the viability of thermal attacks in everyday computer and mobile usage scenarios, 2) develop and evaluate methods for resisting them on desktop and mobile settings, and 3) raise awareness about this threat and possible countermeasures through impact activities that engage with Logitech, a major manufacturer of input peripherals, and local partners such as CENSIS. This project will produce 1) a dataset of thermal images for research on thermal attacks, 2) empirical findings that explain which factors impact the effectiveness of thermal attacks in realistic everyday scenarios in desktop and mobile settings, 3) recommendations for users and manufacturers for resisting thermal attacks on touchscreens and keyboards, 4) a novel machine learning model to be used by researchers and practitioners to analyse the effectiveness of thermal attacks and evaluate countermeasures, 5) a novel machine learning model that predicts vulnerability to thermal attacks and tools that use it to mitigate the risk, and 6) material to raise awareness about thermal attacks and possible countermeasures.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
Date Materialised
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Organisation Website: http://www.gla.ac.uk