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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Dual-Targeted adversarial example in evasion attack on graph neural networks.

Hyun Kwon¹, Dae-Jin Kim²

¹Department of Artificial Intelligence and Data Science, Korea Military Academy, Seoul, 01819, South Korea.

Scientific Reports

|January 31, 2025

Summary

This study introduces dual-targeted adversarial examples for Graph Neural Networks (GNNs), enabling attacks on multiple models simultaneously. This advances graph adversarial attacks and highlights the need for improved GNN defenses.

Keywords:

Adversarial example Evasion attack Graph neural network Machine learning Node classification

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Area of Science:

Artificial Intelligence
Machine Learning
Graph Neural Networks

Background:

Graph Neural Networks (GNNs) are increasingly used in various applications.
Existing adversarial attacks primarily focus on single-model misclassification.
There is a need for more sophisticated attacks that can challenge multiple GNNs.

Purpose of the Study:

To propose a novel method for generating dual-targeted adversarial examples in GNNs.
To enable adversarial attacks that can simultaneously target multiple GNN models with distinct misclassification objectives.
To address limitations in current graph-based adversarial attack techniques.

Main Methods:

Development of a novel approach for creating dual-targeted adversarial examples.
Rigorous evaluation of the proposed method's effectiveness on various GNN models.
Visualization of attack impact using benchmark datasets like Reddit and OGBN-Products.

Main Results:

Demonstration of successful dual-targeted adversarial example generation.
Validation of the approach's capability to affect multiple GNNs with diverse objectives.
Empirical evidence of the disruptive potential of these attacks on GNN performance.

Conclusions:

The proposed dual-targeted adversarial attacks represent a significant advancement in graph-based adversarial research.
These findings underscore the vulnerability of GNNs to sophisticated attacks.
There is an urgent need for developing robust and enhanced defensive strategies for GNNs.