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

Adil Ahmad¹, Anwar Shah², Waleed Alnumay³

¹National University of Computer and Emerging Science, Faisalabad, Pakistan.

Neural Networks : the Official Journal of the International Neural Network Society

|August 27, 2025

Summary

This study introduces a novel method to restore Graph Neural Network (GNN) accuracy after backdoor attacks. The approach uses filtering and augmentation to defend against hidden triggers, achieving high accuracy restoration.

Keywords:

Accuracy restoration Backdoor attacks Graph neural networks Model resilience

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Area of Science:

Artificial Intelligence
Machine Learning Security

Background:

Backdoor attacks threaten machine learning models, especially Graph Neural Networks (GNNs).
Existing defenses often focus on detection rather than accurate restoration of model performance.
The complex structure of graph data complicates GNN defense strategies.

Purpose of the Study:

To develop a method for restoring the original accuracy of GNNs compromised by backdoor attacks.
To enhance GNN resilience against hidden triggers and poisoned inputs.
To improve the interpretability of GNN decision-making post-attack.

Main Methods:

Combining advanced filtering to remove suspicious data points and augmentation to strengthen GNNs against triggers.
Implementing an adaptive framework to balance filtering and augmentation based on attack severity and model sensitivity.
Integrating Explainable AI (XAI) techniques for transparent detection and understanding of backdoor triggers.

Main Results:

Achieved an average accuracy restoration of 97-99% across various backdoor attack scenarios.
Demonstrated effective reduction of false positives and negatives in backdoor detection.
Enhanced GNN integrity and performance in the presence of sophisticated attacks.

Conclusions:

The proposed method offers an effective solution for restoring GNN accuracy after backdoor attacks.
The adaptive filtering and augmentation strategy significantly improves model resilience.
XAI integration enhances transparency and trust in GNN security.