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Derivative-free optimization adversarial attacks for graph convolutional networks
In recent years, graph convolutional networks (GCNs) have emerged rapidly due to their excellent performance in graph data processing. However, recent researches show that GCNs are vulnerable to adversarial attacks. An attacker can maliciously modify edges or nodes of the graph to mislead the model’...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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PeerJ Inc.
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409335/ https://www.ncbi.nlm.nih.gov/pubmed/34541312 http://dx.doi.org/10.7717/peerj-cs.693 |