  Backdoor defense, which aims to detect or mitigate the effect of malicious
triggers introduced by attackers, is becoming increasingly critical for machine
learning security and integrity. Fine-tuning based on benign data is a natural
defense to erase the backdoor effect in a backdoored model. However, recent
studies show that, given limited benign data, vanilla fine-tuning has poor
defense performance. In this work, we provide a deep study of fine-tuning the
backdoored model from the neuron perspective and find that backdoorrelated
neurons fail to escape the local minimum in the fine-tuning process. Inspired
by observing that the backdoorrelated neurons often have larger norms, we
propose FTSAM, a novel backdoor defense paradigm that aims to shrink the norms
of backdoor-related neurons by incorporating sharpness-aware minimization with
fine-tuning. We demonstrate the effectiveness of our method on several
benchmark datasets and network architectures, where it achieves
state-of-the-art defense performance. Overall, our work provides a promising
avenue for improving the robustness of machine learning models against backdoor
attacks.
