Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios

• URL: http://arxiv.org/abs/2202.04781v1
• Date: Thu, 10 Feb 2022 00:47:36 GMT
• Title: Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios
• Authors: Jung Im Choi, Qing Tian
• Abstract summary: We present an effective attack strategy aiming the objectness aspect of visual detection in autonomous vehicles. 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. 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.
• Score: 3.236217153362305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual detection is a key task in autonomous driving, and it serves as one foundation for self-driving planning and control. Deep neural networks have achieved promising results in various computer vision 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 aiming 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.

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