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MAP: MULTISPECTRAL ADVERSARIAL PATCH TO ATTACK PERSON DETECTION

  • Korea Advanced Institute of Science and Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Scopus citations

Abstract

Recently, multispectral person detection has shown great performance in real world applications such as autonomous driving and security systems. However, the reliability of person detection against physical attacks has not been fully explored yet in multispectral person detectors. To evaluate the robustness of multispectral person detectors in the physical world, we propose a novel Multispectral Adversarial Patch (MAP) generation framework. MAP is optimized with a Cross-spectral Mapping(CSM) and Material Emissivity(ME) loss. This paper is the first to evaluate the reliability of a multispectral person detector against physical attack. Throughout experiment, our proposed adversarial patch successfully attacks the person detector and the Average Precision (AP) score is dropped by 90.79% in digital space and 73.34% in physical space.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4853-4857
Number of pages5
ISBN (Electronic)9781665405409
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 - Hybrid, Singapore
Duration: 22 May 202227 May 2022

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May
ISSN (Print)1520-6149

Conference

Conference2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022
Country/TerritorySingapore
CityHybrid
Period22/05/2227/05/22

Keywords

  • Cross-spectral mapping
  • Material emissivity loss
  • Multispectral adversarial patch
  • Person detection
  • Thermal adversarial patch

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