PGRL defends ML models from backdoor attacks by using a few verified clean samples to guide removal of suspicious training data and unlearning of backdoor features during fine-tuning, outperforming prior defenses in experiments.
The perils of learning from unlabeled data: Backdoor attacks on semi-supervised learning
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Prototype-Guided Robust Learning against Backdoor Attacks
PGRL defends ML models from backdoor attacks by using a few verified clean samples to guide removal of suspicious training data and unlearning of backdoor features during fine-tuning, outperforming prior defenses in experiments.