WEIGHTED AVERAGE PRECISION: ADVERSARIAL EXAMPLE DETECTION FOR VISUAL PERCEPTION OF AUTONOMOUS VEHICLES

Weiheng Chai, Yantao Lu, Senem Velipasalar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Recent works have shown that neural networks are vulnerable to carefully crafted adversarial examples (AE). By adding small perturbations to original images, AEs are able to deceive victim models, and result in incorrect outputs. Research work in adversarial machine learning started to focus on the detection of AEs in autonomous driving applications. However, existing studies either use simplifying assumptions on the outputs of object detectors or ignore the tracking system in the perception pipeline. In this paper, we first propose a novel similarity distance metric for object detection outputs in autonomous driving applications. Then, we bridge the gap between the current AE detection research and the real-world autonomous systems by providing a temporal AE detection algorithm, which takes the impact of tracking system into consideration. We perform evaluations on Berkeley Deep Drive and CityScapes datasets, by using different white-box and black-box attacks, which show that our approach outperforms the mean-average-precision and mean intersection-over-union based AE detection baselines by significantly increasing the detection accuracy.

Original languageEnglish (US)
Title of host publication2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PublisherIEEE Computer Society
Pages804-808
Number of pages5
ISBN (Electronic)9781665441155
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: Sep 19 2021Sep 22 2021

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2021-September
ISSN (Print)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States
CityAnchorage
Period9/19/219/22/21

Keywords

  • Adversarial attack
  • Neural networks

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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