%0 Conference Paper %1 9827222 %A Choi, Jung Im %A Tian, Qing %B 2022 IEEE Intelligent Vehicles Symposium (IV) %D 2022 %K ML security %P 1011-1017 %R 10.1109/IV51971.2022.9827222 %T Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios %U https://ieeexplore.ieee.org/document/9827222/ %X 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.

@inproceedings{9827222, abstract = {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.}, added-at = {2023-06-12T11:10:22.000+0200}, author = {Choi, Jung Im and Tian, Qing}, biburl = {https://www.bibsonomy.org/bibtex/201562b8c5fb8bfa07dadb0477458d91d/csl_uth}, booktitle = {2022 IEEE Intelligent Vehicles Symposium (IV)}, doi = {10.1109/IV51971.2022.9827222}, interhash = {94275541e329673956a942f1e0994752}, intrahash = {01562b8c5fb8bfa07dadb0477458d91d}, keywords = {ML security}, month = {June}, pages = {1011-1017}, timestamp = {2023-06-12T11:10:22.000+0200}, title = {Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios}, url = {https://ieeexplore.ieee.org/document/9827222/}, year = 2022 }