Adversarial Attacks and Defenses on Graphs
Wei Jin, Yaxing Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charų C. Aggarwal, Jiliang Tang
Abstract
Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks.
Topics & Concepts
Adversarial systemComputer scienceDeep neural networksArtificial neural networkArtificial intelligenceTheoretical computer scienceComputer securityMachine learningAdversarial Robustness in Machine LearningAdvanced Graph Neural NetworksAnomaly Detection Techniques and Applications