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Recent works have shown that neural networks are vulnerable to carefullycrafted adversarial examples (AE). By adding small perturbations to inputimages, AEs are able to make the victim model predicts incorrect outputs.Several research work in adversarial machine learning started to focus on thedetection of AEs in autonomous driving. However, the existing studies eitheruse preliminary assumption on outputs of detections or ignore the trackingsystem in the perception pipeline. In this paper, we firstly propose a noveldistance metric for practical autonomous driving object detection outputs.Then, we bridge the gap between the current AE detection research and thereal-world autonomous systems by providing a temporal detection algorithm,which takes the impact of tracking system into consideration. We performevaluation on Berkeley Deep Drive (BDD) and CityScapes datasets to show how ourapproach outperforms existing single-frame-mAP based AE detections byincreasing 17.76% accuracy of performance.

Weighted Average Precision: Adversarial Example Detection in the Visual Perception of Autonomous Vehicles