  In the era of increasing concerns over cybersecurity threats, defending
against backdoor attacks is paramount in ensuring the integrity and reliability
of machine learning models. However, many existing approaches require
substantial amounts of data for effective mitigation, posing significant
challenges in practical deployment. To address this, we propose a novel
approach to counter backdoor attacks by treating their mitigation as an
unlearning task. We tackle this challenge through a targeted model pruning
strategy, leveraging unlearning loss gradients to identify and eliminate
backdoor elements within the model. Built on solid theoretical insights, our
approach offers simplicity and effectiveness, rendering it well-suited for
scenarios with limited data availability. Our methodology includes formulating
a suitable unlearning loss and devising a model-pruning technique tailored for
convolutional neural networks. Comprehensive evaluations demonstrate the
efficacy of our proposed approach compared to state-of-the-art approaches,
particularly in realistic data settings.
