A graph backdoor detection method for data collection scenarios
Abstract Data collection is an effective way to build a better Graph Neural Network (GNN) model, but it also makes it easy for attackers to implant backdoors into the model through data poisoning. In this work, we propose a backdoor detection method of graph for data collection scenarios (CGBD). Dif...
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Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
SpringerOpen
2025-01-01
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Series: | Cybersecurity |
Subjects: | |
Online Access: | https://doi.org/10.1186/s42400-024-00305-w |
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