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arXiv Computer Vision By Arnold Brosch, Kira Maag

RePAIR: Inverting Backdoor Attacks in Semantic Segmentation

Read the original on arXiv Computer Vision →

RePAIR is a post‑hoc, label‑free defense for semantic segmentation models that counters backdoor attacks by inferring victim‑target relationships from unlabeled images and correcting predictions in feature space and output space. It localizes suspicious pixels by comparing classifier head outputs with internal semantic evidence, applying conservative constraints to avoid harming clean predictions. Experiments demonstrate that RePAIR significantly reduces attack success across various segmentation architectures while largely preserving clean performance.

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