ODPure: Backdoor Purification for Object Detection via Ensemble Corruption Consensus
ODPure purifies backdoor triggers in object detectors via corruption, reconstruction, and voting without discarding models.
Researchers propose ODPure, a black-box input-stage defense for object detectors compromised by backdoors. Triggers can misclassify objects, remove bounding boxes, or generate detections for objects that are not present. Its Corruption-Reconstruction-Selection pipeline applies diverse corruptions, restores structural detail with generative priors, and uses voting to agree on detections. Experiments claim robust defense across attack and trigger types while keeping baseline accuracy; code is published on GitHub.
- Object-detection backdoors can misclassify, hide, or invent objects.
- ODPure purifies inputs instead of discarding data or models.
- CRS corrupts inputs, reconstructs structure, then votes on detections.
- Authors report preserved accuracy across attacks and trigger types.
From the original article
1 min read at arxiv.orgWith the development of applications like autonomous driving, object detection has gained significant attention, while also highlighting critical vulnerabilities like backdoor attacks that severely compromise model integrity. Specifically, such attacks involve altering the categories of objects (i. e.
By Li Zeng · arxiv.org
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