Towards Adversarially Robust Object Detection

2019

161 citations recorded by the source corpus.

Object detection is an important vision task and has emerged as an indispensable component in many vision system, rendering its robustness as an increasingly important performance factor for practical applications. While object detection models have been demonstrated to be vulnerable against adversarial attacks by many recent works, very few efforts have been devoted to improving their robustness. In this work, we take an initial attempt towards this direction. We first revisit and systematically analyze object detectors and many recently developed attacks from the perspective of model robustness. We then present a multi-task learning perspective of object detection and identify an asymmetric role of task losses. We further develop an adversarial training approach which can leverage the multiple sources of attacks for improving the robustness of detection models. Extensive experiments on PASCAL-VOC and MS-COCO verified the effectiveness of the proposed approach.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

Citations in the corpus

Do Image Classifiers Generalize Across Time? (2019, background): “While various forms of adversarial examples have been studied, the majority of research focuses on ℓ p robustness [11, 2, 32].”
Rethinking Classification and Localization for Object Detection (2019, background): “Zhang and Wang [45] point out that there exist mis-alignments between classiﬁcation and localization task domains.”
Advances in adversarial attacks and defenses in computer vision: A survey (2021, background): “semantic segmentation [23], [24]; object detection [25], [26]; and object tracking [27], [28].”
UP-DETR: Unsupervised Pre-training for Object Detection with Transformers (2020, background): “Object detection is the coupling of object classification and localization, where these two tasks always have different feature preferences [45, 40, 35].”
Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks (2021, methodology): “Adversarial training (AT) [106].”
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training (2019, background): “Many encouraging progresses been made towards improving model robustness against adversarial examples under different scenarios [58, 36, 33, 67, 72, 16, 71].”