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Title: A framework for evaluating image obfuscation under deep learning-assisted privacy attacks
Authors: Tekli, Jimmy
Bouna, Bechara al
Couturier, Raphaël
Tekli, Gilbert 
Zeinab, al Zein
Kamradt, Marc
Affiliations: Department of Mechatronics Engineering 
Keywords: Data privacy
Face obfuscation
Deep learning
Image transformations
Issue Date: 2019
Part of: 2019 17th International Conference on Privacy, Security and Trust (PST)
Start page: 1
End page: 10
Conference: International Conference on Privacy, Security and Trust (PST) (17th : 26-28 Aug. 2019 : Fredericton, NB, Canada) 
Computer 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.
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Mechatronics Engineering

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