PART-BASED FEATURE SQUEEZING TO DETECT ADVERSARIAL EXAMPLES IN PERSON RE-IDENTIFICATION NETWORKS

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

3 Scopus citations

Abstract

Although deep neural networks (DNNs) have achieved top performances in different computer vision tasks, such as object detection, image segmentation and person re-identification (ReID), they can easily be deceived by adversarial examples, which are carefully crafted images with perturbations that are imperceptible to human eyes. Such adversarial examples can significantly degrade the performance of existing DNNs. There are also targeted attacks misleading classifiers into making specific decisions based on attackers’ intentions. In this paper, we propose a new method to effectively detect adversarial examples presented to a person ReID network. The proposed method utilizes parts-based feature squeezing to detect the adversarial examples. We apply two types of squeezing to segmented body parts to better detect adversarial examples. We perform extensive experiments over three major datasets with different attacks, and compare the detection performance of the proposed body part-based approach with a ReID method that is not parts-based. Experimental results show that the proposed method can effectively detect the adversarial examples, and has the potential to avoid significant decreases in person ReID performance caused by adversarial examples.

Original languageEnglish (US)
Title of host publication2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PublisherIEEE Computer Society
Pages844-848
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
  • Adversarial example
  • DNN
  • Person re-identification
  • ReID

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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