Universal adversarial attacks on graph neural networks

Universal adversarial attacks on graph neural networks

This project aims to study the robustness of graph-level graph neural networks (GNNs) against universal adversarial attacks in white-box and grey-box scenarios. Graph-level GNNs are widely used in critical domains such as quantum chemistry, drug discovery, while the robustness of them against un...

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書目詳細資料
主要作者:	Liao, Chang
其他作者:	Tay, Wee Peng
格式:	Final Year Project
語言:	English
出版:	Nanyang Technological University 2021
主題:	Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering
在線閱讀:	https://hdl.handle.net/10356/149401
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

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