Trap: Mitigating Poisoning-Based Backdoor Attacks by Treating Poison With Poison

Summary

Researchers propose a training method that detects poisoned samples in the early stages of training and removes the backdoor by retraining the classifier part of the model on relabeled poisoned samples. Evaluated against twelve attacks on four datasets, it reduced the average attack success rate to 0.07% while lowering average accuracy by 0.33%.

Mitigation

The proposed defense detects poisoned samples early in training and removes the backdoor by retraining the classifier part of the model on relabeled poisoned samples.

Topics

• Adversarial machine learning

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