<ul><li>Despite advancements in Graph Neural Networks (GNNs), adaptive attacks continue to challenge their robustness.</li><li>Certified robustness based on randomized smoothing has emerged as a promising solution, offering provable guarantees that a model's predictions remain stable under adversarial perturbations.</li><li>The proposed framework, AuditVotes, integrates randomized smoothing with augmentation and conditional smoothing to improve data quality and prediction consistency.</li><li>Experimental results demonstrate that AuditVotes significantly enhances clean accuracy, certified robustness, and empirical robustness for GNNs.</li></ul>

AuditVotes: A Framework Towards More Deployable Certified Robustness for Graph Neural Networks

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