Dual-Targeted adversarial example in evasion attack on graph neural networks
Abstract This study proposes a novel approach for generating dual-targeted adversarial examples in Graph Neural Networks (GNNs), significantly advancing the field of graph-based adversarial attacks. Unlike traditional methods that focus on inducing specific misclassifications in a single model, our...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85493-2 |
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| author | Hyun Kwon Dae-Jin Kim |
| author_facet | Hyun Kwon Dae-Jin Kim |
| author_sort | Hyun Kwon |
| collection | DOAJ |
| description | Abstract This study proposes a novel approach for generating dual-targeted adversarial examples in Graph Neural Networks (GNNs), significantly advancing the field of graph-based adversarial attacks. Unlike traditional methods that focus on inducing specific misclassifications in a single model, our approach creates adversarial samples that can simultaneously target multiple models, each inducing distinct misclassifications. This innovation addresses a critical gap in existing techniques by enabling adversarial attacks that are capable of affecting various models with different objectives. We provide a detailed explanation of the method’s principles and structure, rigorously evaluate its effectiveness across several GNN models, and visualize the impact using datasets such as Reddit and OGBN-Products. Our contributions highlight the potential for dual-targeted attacks to disrupt GNN performance and emphasize the need for enhanced defensive strategies in graph-based learning systems. |
| format | Article |
| id | doaj-art-72b9b956c11249d6b86d27f5d007fb6e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-72b9b956c11249d6b86d27f5d007fb6e2025-02-02T12:17:33ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-025-85493-2Dual-Targeted adversarial example in evasion attack on graph neural networksHyun Kwon0Dae-Jin Kim1Department of Artificial Intelligence and Data Science, Korea Military AcademyDepartment of Architectural Engineering, Kyung Hee UniversityAbstract This study proposes a novel approach for generating dual-targeted adversarial examples in Graph Neural Networks (GNNs), significantly advancing the field of graph-based adversarial attacks. Unlike traditional methods that focus on inducing specific misclassifications in a single model, our approach creates adversarial samples that can simultaneously target multiple models, each inducing distinct misclassifications. This innovation addresses a critical gap in existing techniques by enabling adversarial attacks that are capable of affecting various models with different objectives. We provide a detailed explanation of the method’s principles and structure, rigorously evaluate its effectiveness across several GNN models, and visualize the impact using datasets such as Reddit and OGBN-Products. Our contributions highlight the potential for dual-targeted attacks to disrupt GNN performance and emphasize the need for enhanced defensive strategies in graph-based learning systems.https://doi.org/10.1038/s41598-025-85493-2Graph neural networkAdversarial exampleEvasion attackNode classificationMachine learning |
| spellingShingle | Hyun Kwon Dae-Jin Kim Dual-Targeted adversarial example in evasion attack on graph neural networks Scientific Reports Graph neural network Adversarial example Evasion attack Node classification Machine learning |
| title | Dual-Targeted adversarial example in evasion attack on graph neural networks |
| title_full | Dual-Targeted adversarial example in evasion attack on graph neural networks |
| title_fullStr | Dual-Targeted adversarial example in evasion attack on graph neural networks |
| title_full_unstemmed | Dual-Targeted adversarial example in evasion attack on graph neural networks |
| title_short | Dual-Targeted adversarial example in evasion attack on graph neural networks |
| title_sort | dual targeted adversarial example in evasion attack on graph neural networks |
| topic | Graph neural network Adversarial example Evasion attack Node classification Machine learning |
| url | https://doi.org/10.1038/s41598-025-85493-2 |
| work_keys_str_mv | AT hyunkwon dualtargetedadversarialexampleinevasionattackongraphneuralnetworks AT daejinkim dualtargetedadversarialexampleinevasionattackongraphneuralnetworks |