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dc.contributor.authorTekli, Jimmyen_US
dc.contributor.authorBouna, Bechara alen_US
dc.contributor.authorCouturier, Raphaëlen_US
dc.contributor.authorTekli, Gilberten_US
dc.contributor.authorZeinab, al Zeinen_US
dc.contributor.authorKamradt, Marcen_US
dc.description.abstractComputer vision applications such as object detection and recognition, allow machines to visualize and perceive their environments. Nevertheless, these applications are guided by learning-based methods that require capturing, storing and processing large amounts of images thus rendering privacy and anonymity a major concern. In return, image obfuscation techniques (i.e., pixelating, blurring, and masking) have been developed to protect the sensitive information in images. In this paper, we propose a framework to evaluate and recommend the most robust obfuscation techniques in a specific domain of application. The proposed framework reconstructs obfuscated faces via deep learning-assisted attacks and assesses the reconstructions using structural/identity-based metrics. To evaluate and validate our approach, we conduct our experiments on a publicly available celebrity faces dataset. The obfuscation techniques considered are pixelating, blurring and masking. We evaluate the faces reconstructions against five deep learning-assisted privacy attackers. The most resilient obfuscation technique is recommended with regard to structural and identity-based metrics.en_US
dc.format.extent10 p.en_US
dc.subjectData privacyen_US
dc.subjectFace obfuscationen_US
dc.subjectDeep learningen_US
dc.subjectImage transformationsen_US
dc.titleA framework for evaluating image obfuscation under deep learning-assisted privacy attacksen_US
dc.typeConference Paperen_US
dc.relation.conferenceInternational Conference on Privacy, Security and Trust (PST) (17th : 26-28 Aug. 2019 : Fredericton, NB, Canada)en_US
dc.contributor.affiliationDepartment of Mechatronics Engineeringen_US
dc.relation.ispartoftext2019 17th International Conference on Privacy, Security and Trust (PST)en_US
dc.provenance.recordsourceOliben_US Fares Faculty of Technology-
Appears in Collections:Department of Mechatronics Engineering
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