UAI 2023poster3 citations
MFA: Multi-layer Feature-aware Attack for Object Detection
Wen Chen, Yushan Zhang, Zhiheng Li, Yuehuan Wang
Abstract
Physical adversarial attacks can mislead detectors in real-world scenarios and have attracted increasing attention. However, most existing works manipulate the detector’s final outputs as attack targets while ignoring the inherent characteristics of objects. This can result in attacks being trapped in model-specific local optima and reduced transferability. To address this issue, we propose a
BibTeX
@InProceedings{pmlr-v216-chen23d,
title = {{MFA}: Multi-layer Feature-aware Attack for Object Detection},
author = {Chen, Wen and Zhang, Yushan and Li, Zhiheng and Wang, Yuehuan},
booktitle = {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence},
pages = {336--346},
year = {2023},
editor = {Evans, Robin J. and Shpitser, Ilya},
volume = {216},
series = {Proceedings of Machine Learning Research},
month = {31 Jul--04 Aug},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v216/chen23d/chen23d.pdf},
url = {https://proceedings.mlr.press/v216/chen23d.html},
abstract = {Physical adversarial attacks can mislead detectors in real-world scenarios and have attracted increasing attention. However, most existing works manipulate the detector’s final outputs as attack targets while ignoring the inherent characteristics of objects. This can result in attacks being trapped in model-specific local optima and reduced transferability. To address this issue, we propose a