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Found 3 papers, 3 papers with code
Reconstructive Neuron Pruning for Backdoor Defense
Specifically, RNP first unlearns the neurons by maximizing the model's error on a small subset of clean samples and then recovers the neurons by minimizing the model's error on the same data.
Anti-Backdoor Learning: Training Clean Models on Poisoned Data
From this view, we identify two inherent characteristics of backdoor attacks as their weaknesses: 1) the models learn backdoored data much faster than learning with clean data, and the stronger the attack the faster the model converges on backdoored data; 2) the backdoor task is tied to a specific class (the backdoor target class).
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks
NAD utilizes a teacher network to guide the finetuning of the backdoored student network on a small clean subset of data such that the intermediate-layer attention of the student network aligns with that of the teacher network.
