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Reinforcement Learning For Data Poisoning on Graph Neural Networks
[article]
2021
arXiv
pre-print
Adversarial Machine Learning has emerged as a substantial subfield of Computer Science due to a lack of robustness in the models we train along with crowdsourcing practices that enable attackers to tamper with data. In the last two years, interest has surged in adversarial attacks on graphs yet the Graph Classification setting remains nearly untouched. Since a Graph Classification dataset consists of discrete graphs with class labels, related work has forgone direct gradient optimization in
arXiv:2102.06800v1
fatcat:xme7cul6wfdkpgre6efsu2q4hu