  Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks,
posing concerning threats to their reliable deployment. Recent research reveals
that backdoors can be erased from infected DNNs by pruning a specific group of
neurons, while how to effectively identify and remove these backdoor-associated
neurons remains an open challenge. Most of the existing defense methods rely on
defined rules and focus on neuron's local properties, ignoring the exploration
and optimization of pruning policies. To address this gap, we propose an
Optimized Neuron Pruning (ONP) method combined with Graph Neural Network (GNN)
and Reinforcement Learning (RL) to repair backdoor models. Specifically, ONP
first models the target DNN as graphs based on neuron connectivity, and then
uses GNN-based RL agents to learn graph embeddings and find a suitable pruning
policy. To the best of our knowledge, this is the first attempt to employ GNN
and RL for optimizing pruning policies in the field of backdoor defense.
Experiments show, with a small amount of clean data, ONP can effectively prune
the backdoor neurons implanted by a set of backdoor attacks at the cost of
negligible performance degradation, achieving a new state-of-the-art
performance for backdoor mitigation.
