LiDAR-Adv generates adversarial objects to fool LiDAR-based autonomous driving detection systems, tested on Baidu Apollo and with physical 3D prints.
Moosavi-Dezfooli, A
2 Pith papers cite this work. Polarity classification is still indexing.
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2019 2verdicts
UNVERDICTED 2representative citing papers
A DIP-based optimization produces adversarial perturbations and patches that are more robust to affine transformations than standard high-frequency noise while staying imperceptible.
citing papers explorer
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Adversarial Objects Against LiDAR-Based Autonomous Driving Systems
LiDAR-Adv generates adversarial objects to fool LiDAR-based autonomous driving detection systems, tested on Baidu Apollo and with physical 3D prints.
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Robust Synthesis of Adversarial Visual Examples Using a Deep Image Prior
A DIP-based optimization produces adversarial perturbations and patches that are more robust to affine transformations than standard high-frequency noise while staying imperceptible.