  The application of deep neural network models in various security-critical
applications has raised significant security concerns, particularly the risk of
backdoor attacks. Neural backdoors pose a serious security threat as they allow
attackers to maliciously alter model behavior. While many defenses have been
explored, existing approaches are often bounded by model-specific constraints,
or necessitate complex alterations to the training process, or fall short
against diverse backdoor attacks. In this work, we introduce a novel method for
comprehensive and effective elimination of backdoors, called ULRL (short for
UnLearn and ReLearn for backdoor removal). ULRL requires only a small set of
clean samples and works effectively against all kinds of backdoors. It first
applies unlearning for identifying suspicious neurons and then targeted neural
weight tuning for backdoor mitigation (i.e., by promoting significant weight
deviation on the suspicious neurons). Evaluated against 12 different types of
backdoors, ULRL is shown to significantly outperform state-of-the-art methods
in eliminating backdoors whilst preserving the model utility.
