Privacy-preserving Object Detection

• URL: http://arxiv.org/abs/2103.06587v1
• Date: Thu, 11 Mar 2021 10:34:54 GMT
• Title: Privacy-preserving Object Detection
• Authors: Peiyang He, Charlie Griffin, Krzysztof Kacprzyk, Artjom Joosen, Michael Collyer, Aleksandar Shtedritski, Yuki M. Asano
• Abstract summary: 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.
• Score: 52.77024349608834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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