GraphGuard: An adaptive approach for restoring accuracy in backdoor-compromised GNNs.

Abstract

Backdoor attacks present a significant threat to the reliability of machine learning models, including Graph Neural Networks (GNNs), by embedding triggers that manipulate model behavior. While many existing defenses focus on identifying these vulnerabilities, few address restoring model accuracy after an attack. This paper introduces a method for restoring the original accuracy of GNNs affected by backdoor attacks, a task complicated by the complex structure of graph data. Our approach combines advanced filtering and augmentation techniques that enhance the GNN's resilience against hidden triggers. The filtering mechanisms remove suspicious data points to minimize the influence of poisoned inputs, while augmentation introduces controlled variation to strengthen the model against backdoor triggers. To optimize restoration, we present an adaptive framework that adjusts the balance between filtering and augmentation based on model sensitivity and attack severity, reducing both false positives and negatives. Additionally, we incorporate Explainable AI (XAI) techniques to improve the interpretability of the model's decision-making process, enabling transparent detection and understanding of backdoor triggers. Results demonstrate that our method achieves an average accuracy restoration of 97-99 % across various backdoor attack scenarios, providing an effective solution to maintain the performance and integrity of GNNs in sensitive applications.