  Visual detection is a key task in autonomous driving, and it serves as a
crucial foundation for self-driving planning and control. Deep neural networks
have achieved promising results in various visual tasks, but they are known to
be vulnerable to adversarial attacks. A comprehensive understanding of deep
visual detectors' vulnerability is required before people can improve their
robustness. However, only a few adversarial attack/defense works have focused
on object detection, and most of them employed only classification and/or
localization losses, ignoring the objectness aspect. In this paper, we identify
a serious objectness-related adversarial vulnerability in YOLO detectors and
present an effective attack strategy targeting the objectness aspect of visual
detection in autonomous vehicles. Furthermore, to address such vulnerability,
we propose a new objectness-aware adversarial training approach for visual
detection. Experiments show that the proposed attack targeting the objectness
aspect is 45.17% and 43.50% more effective than those generated from
classification and/or localization losses on the KITTI and COCO traffic
datasets, respectively. Also, the proposed adversarial defense approach can
improve the detectors' robustness against objectness-oriented attacks by up to
21% and 12% mAP on KITTI and COCO traffic, respectively.
