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Type: Conference paper
Title: Physical Adversarial Attacks on an Aerial Imagery Object Detector
Author: Du, A.
Chen, B.
Chin, T.J.
Law, Y.W.
Sasdelli, M.
Raja Segaran, R.
Campbell, D.
Citation: Proceedings 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2022), 2022, pp.3798-3808
Publisher: IEEE
Publisher Place: Online
Issue Date: 2022
Series/Report no.: IEEE Winter Conference on Applications of Computer Vision
ISBN: 9781665409162
ISSN: 2472-6737
Conference Name: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (4 Jan 2022 - 8 Jan 2022 : Waikoloa, Hawaii)
Statement of
Andrew Du, Bo Chen, Tat-Jun Chin, Yee Wei Law, Michele Sasdelli, Ramesh Rajasegaran, Dillon Campbell
Abstract: Deep neural networks (DNNs) have become essential for processing the vast amounts of aerial imagery collected using earth-observing satellite platforms. However, DNNs are vulnerable towards adversarial examples, and it is expected that this weakness also plagues DNNs for aerial imagery. In this work, we demonstrate one of the first efforts on physical adversarial attacks on aerial imagery, whereby adversarial patches were optimised, fabricated and installed on or near target objects (cars) to significantly reduce the efficacy of an object detector applied on overhead images. Physical adversarial attacks on aerial images, particularly those captured from satellite platforms, are challenged by atmospheric factors (lighting, weather, seasons) and the distance between the observer and target. To investigate the effects of these challenges, we devised novel experiments and metrics to evaluate the efficacy of physical adversarial attacks against object detectors in aerial scenes. Our results indicate the palpable threat posed by physical adversarial attacks towards DNNs for processing satellite imagery.
Rights: ©2021 IEEE
DOI: 10.1109/WACV51458.2022.00385
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Appears in Collections:Computer Science publications

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