Mirage: a Clean-Label Backdoor against LiDAR 3D Object Detection
Deep neural network-based LiDAR 3D object detection serves as a critical perception component in safety-critical autonomous systems. However, recent studies have revealed its vulnerability to backdoor attacks. Existing attacks typically require white-box access or label modification and focus on geometric attacks such as object disappearance or bounding-box manipulation. In this paper, we present Mirage, a black-box and clean-label backdoor attack against deep neural network-based LiDAR 3DOD. Mirage injects a small number of label-consistent poisoning samples into the training set, causing the model to learn a malicious association between a trigger pattern and an attacker-chosen target c...
| Authors | Ziba Parsons, Ang Li |
|---|---|
| Primary category | cs.CV |
| Published | 18 June 2026 |
| Last updated at source | 28 September 2026 |
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