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“Healthy surveillance”: Designing a concept for privacy-preserving mask recognition AI in the age of pandemics

Kühl, Niklas; Martin, Dominik; Wolff, Clemens; Volkamer, Melanie

Abstract (englisch):
The obligation to wear masks in times of pandemics reduces the risk of spreading viruses. In case of the COVID-19 pandemic in 2020, many governments recommended or even obligated their citizens to wear masks as an effective countermeasure. In order to continuously monitor the compliance of this policy measure in public spaces like restaurants or tram stations by public authorities, one scalable and automatable option depicts the application of surveillance systems, i.e., CCTV. However, large-scale monitoring of mask recognition does not only require a well-performing Artificial Intelligence, but also ensure that no privacy issues are introduced, as surveillance is a deterrent for citizens and regulations like GDPR demand strict regulations of such personal data. In this work, we show how a privacy-preserving mask recognition artifact could look like, demonstrate different options for implementation and evaluate performances. Our conceptual deep-learning based Artificial Intelligence is able to achieve detection performances between 95% and 99% in a privacy-friendly setting. On that basis, we elaborate on the trade-off between the level of privacy preservation and Artificial Intelligence performance, i.e. ... mehr

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Volltext §
DOI: 10.5445/IR/1000120631
Veröffentlicht am 30.06.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Forschungsbericht/Preprint
Publikationsdatum 28.06.2020
Sprache Englisch
Identifikator KITopen-ID: 1000120631
Verlag KIT, Karlsruhe
Serie KIT Working Paper
Schlagwörter Video surveillance, mask recognition, pandemic, covid-19, privacy-preservation, artificial intelligence
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