Combining Two Adversarial Attacks Against Person Re-Identification Systems

Eduardo Andrade, Igor Sampaio, Joris Guérin, José Viterbo

2023

Abstract

The field of Person Re-Identification (Re-ID) has received much attention recently, driven by the progress of deep neural networks, especially for image classification. The problem of Re-ID consists in identifying individuals through images captured by surveillance cameras in different scenarios. Governments and companies are investing a lot of time and money in Re-ID systems for use in public safety and identifying missing persons. However, several challenges remain for successfully implementing Re-ID, such as occlusions and light reflections in people’s images. In this work, we focus on adversarial attacks on Re-ID systems, which can be a critical threat to the performance of these systems. In particular, we explore the combination of adversarial attacks against Re-ID models, trying to strengthen the decrease in the classification results. We conduct our experiments on three datasets: DukeMTMC-ReID, Market-1501, and CUHK03. We combine the use of two types of adversarial attacks, P-FGSM and Deep Mis-Ranking, applied to two popular Re-ID models: IDE (ResNet-50) and AlignedReID. The best result demonstrates a decrease of 3.36% in the Rank-10 metric for AlignedReID applied to CUHK03. We also try to use Dropout during the inference as a defense method.

Paper Citation

in Harvard Style

Andrade E., Sampaio I., Guérin J. and Viterbo J. (2023). Combining Two Adversarial Attacks Against Person Re-Identification Systems. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 437-444. DOI: 10.5220/0011623800003417

in Bibtex Style

@conference{visapp23,
author={Eduardo Andrade and Igor Sampaio and Joris Guérin and José Viterbo},
title={Combining Two Adversarial Attacks Against Person Re-Identification Systems},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={437-444},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011623800003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Combining Two Adversarial Attacks Against Person Re-Identification Systems
SN - 978-989-758-634-7
AU - Andrade E.
AU - Sampaio I.
AU - Guérin J.
AU - Viterbo J.
PY - 2023
SP - 437
EP - 444
DO - 10.5220/0011623800003417
PB - SciTePress