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Adversarial Vulnerability of Temporal Feature Networks for Object Detection. (arXiv:2208.10773v1 [cs.CV])
Aug. 24, 2022, 1:20 a.m. | Svetlana Pavlitskaya, Nikolai Polley, Michael Weber, J.Marius Zöllner
cs.CR updates on arXiv.org arxiv.org
Taking into account information across the temporal domain helps to improve
environment perception in autonomous driving. However, it has not been studied
so far whether temporally fused neural networks are vulnerable to deliberately
generated perturbations, i.e. adversarial attacks, or whether temporal history
is an inherent defense against them. In this work, we study whether temporal
feature networks for object detection are vulnerable to universal adversarial
attacks. We evaluate attacks of two types: imperceptible noise for the whole
image and locally-bound …
adversarial detection networks object temporal vulnerability
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