3 papers
cs.LG2022
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation
Zhihao Zhu, Chenwang Wu, Min Zhou +3
Recent studies show that Graph Neural Networks(GNNs) are vulnerable and easily fooled by small perturbations, which has raised considerable concerns for adapting GNNs in various sa…
cs.LG2022
Towards Robust Recommender Systems via Triple Cooperative Defense
Qingyang Wang, Defu Lian, Chenwang Wu +1
Recommender systems are often susceptible to well-crafted fake profiles, leading to biased recommendations. The wide application of recommender systems makes studying the defense a…
cs.CR2019
Random Directional Attack for Fooling Deep Neural Networks
Wenjian Luo, Chenwang Wu, Nan Zhou +1
Deep neural networks (DNNs) have been widely used in many fields such as images processing, speech recognition; however, they are vulnerable to adversarial examples, and this is a…