2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
Talos: A More Effective and Efficient Adversarial Defense for GNN Models Based on the Global Homophily of Graphs
Duanyu Li, Huijun Wu, Min Xie +3
Graph neural network (GNN) models play a pivotal role in numerous tasks involving graph-related data analysis. Despite their efficacy, similar to other deep learning models, GNNs a…
cs.LG2021
CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph
Xugang Wu, Huijun Wu, Xu Zhou +1
Graph neural networks exhibit remarkable performance in graph data analysis. However, the robustness of GNN models remains a challenge. As a result, they are not reliable enough to…
cs.CV2019★ 2 cited
STA: Adversarial Attacks on Siamese Trackers
Xugang Wu, Xiaoping Wang, Xu Zhou +1
Recently, the majority of visual trackers adopt Convolutional Neural Network (CNN) as their backbone to achieve high tracking accuracy. However, less attention has been paid to the…