Identification of subjects from reconstructed images

Identification of individual subjects based on image reconstructions generated from fMRI brain scans

Bachelor thesis (2023)

Authors

A.G. Mercier Electrical Engineering, Mathematics and Computer Science

Contributors

X. Zhang Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
N. Tömen Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Arthur Mercier
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Published Date

27-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Reconstructing seen images from functional magnetic resonance imaging (fMRI) brain scans has been a growing topic of interest in the field of neuroscience, fostered by innovation in machine learning and AI. This paper investigates the possible presence of personal features allowing the identification of subjects from their reconstructed images. Identifying the extent to which personal information is present is necessary to prevent privacy and data protection breaches. Additionally, personal features may reveal information about how people see the world, furthering work in computer-brain interfacing or helping people with neurological conditions that affect sight. In this paper, a CNN model is presented that allows to identify subjects from their reconstructed image with an average accuracy of 90.4%. An encoder-decoder model was used to produce the reconstructed images from the Generic Object Data set. The accuracy shows that personal features are indeed present in the reconstructed images, raising important ethical and legal considerations when using image reconstruction technology.