  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.
