Privacy-preserving Object Detection

Aleksandar Shtedritski; Artjom Joosen; Charlie Griffin; Krzysztof Kacprzyk; Michael Collyer; Peiyang He; Yuki M. Asano

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arxiv 2103.06587 v1 pith:LOYWVUBS submitted 2021-03-11 cs.CV

Privacy-preserving Object Detection

Peiyang He , Charlie Griffin , Krzysztof Kacprzyk , Artjom Joosen , Michael Collyer , Aleksandar Shtedritski , Yuki M. Asano This is my paper

classification cs.CV

keywords detectionobjectbiasdatasetsfacesprivacyalonganonymizing

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Privacy considerations and bias in datasets are quickly becoming high-priority issues that the computer vision community needs to face. So far, little attention has been given to practical solutions that do not involve collection of new datasets. In this work, we show that for object detection on COCO, both anonymizing the dataset by blurring faces, as well as swapping faces in a balanced manner along the gender and skin tone dimension, can retain object detection performances while preserving privacy and partially balancing bias.

Privacy-preserving Object Detection

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