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.

翻译：隐私考虑和数据集中的偏见正在迅速成为计算机视觉界需要面对的高度优先问题,迄今为止,很少注意不涉及收集新数据集的实际解决办法。 在这项工作中,我们表明,COCO的物体探测,既通过模糊面孔将数据集匿名,又按照性别和皮肤基调平衡地互换面孔,既可以保留物体探测性能,同时又保持隐私和部分平衡偏见。