Universal adversarial attacks on graph neural networks

• المؤلف الرئيسي: Liao, Chang
• مؤلفون آخرون: Tay, Wee Peng
• التنسيق: Final Year Project
• اللغة: English
• منشور في: Nanyang Technological University 2021
• الموضوعات: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; Engineering::Electrical and electronic engineering
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/149401

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...