Unlearning Backdoor Attacks through Gradient-Based Model Pruning
This addresses cybersecurity threats for machine learning practitioners, offering a practical solution in data-scarce scenarios, though it appears incremental as it builds on existing unlearning and pruning concepts.
The paper tackles the problem of defending against backdoor attacks in machine learning models by proposing a gradient-based model pruning approach for unlearning backdoors, achieving effectiveness with limited data availability.
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.