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dc.contributor.authorHassan, Muhammad Umair
dc.contributor.authorStava, Magnus
dc.contributor.authorHameed, Ibrahim A
dc.date.accessioned2024-02-21T07:39:08Z
dc.date.available2024-02-21T07:39:08Z
dc.date.created2024-01-04T15:31:03Z
dc.date.issued2023
dc.identifier.citation2023 International Conference on Smart Applications, Communications and Networking (SmartNets)en_US
dc.identifier.isbn979-8-3503-0252-3
dc.identifier.urihttps://hdl.handle.net/11250/3118835
dc.description.abstractInterest in privacy has increased due to the public’s increased attention given to it by the introduction of the EU’s GDPR. The number of images containing identifiable features has multiplied dramatically in an increasingly digital world where data is gathered on a large scale through surveillance systems, smartphones, cameras, etc. In order to protect our privacy, it is essential to look into methods that can anonymize individuals in real time before the digital data is stored. We look into two state-of-the-art face detectors and consider how they perform in real time. In addition, we consider multiple methods for anonymizing individuals in the loop and how it affects the resulting image. The performance is based on the WiderFace benchmark, including easy, medium, and hard subsets.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.titleDeep Privacy based Face Anonymization for Smart Citiesen_US
dc.title.alternativeDeep Privacy based Face Anonymization for Smart Citiesen_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.journalIEEE Xplore Digital Libraryen_US
dc.identifier.doi10.1109/SmartNets58706.2023.10215996
dc.identifier.cristin2220797
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode0


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