  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. In this paper, we investigate the
correlation between backdoor behavior and neuron magnitude, and find that
backdoor neurons deviate from the magnitude-saliency correlation of the model.
The deviation inspires us to propose a Magnitude-based Neuron Pruning (MNP)
method to detect and prune backdoor neurons. Specifically, MNP uses three
magnitude-guided objective functions to manipulate the magnitude-saliency
correlation of backdoor neurons, thus achieving the purpose of exposing
backdoor behavior, eliminating backdoor neurons and preserving clean neurons,
respectively. Experiments show our pruning strategy achieves state-of-the-art
backdoor defense performance against a variety of backdoor attacks with a
limited amount of clean data, demonstrating the crucial role of magnitude for
guiding backdoor defenses.
