30 citations · 44 across the 16 of their papers we have counts for
5 papers · 1 filter
Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field
Keke Tang, Weiyao Ke, Weilong Peng +5
Adversarial attacks on point clouds are crucial for assessing and improving the adversarial robustness of 3D deep learning models. Traditional solutions strictly limit point displa…
Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges
Meixia He, Peican Zhu, Keke Tang +1
Recent studies have shown that Hypergraph Neural Networks (HGNNs) are vulnerable to adversarial attacks. Existing approaches focus on hypergraph modification attacks guided by grad…
Node Injection Attack Based on Label Propagation Against Graph Neural Network
Peican Zhu, Zechen Pan, Keke Tang +3
Graph Neural Network (GNN) has achieved remarkable success in various graph learning tasks, such as node classification, link prediction and graph classification. The key to the su…
A General Black-box Adversarial Attack on Graph-based Fake News Detectors
Peican Zhu, Zechen Pan, Yang Liu +3
Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the constructi…
GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion
Le Cheng, Peican Zhu, Keke Tang +2
Source detection in graphs has demonstrated robust efficacy in the domain of rumor source identification. Although recent solutions have enhanced performance by leveraging deep neu…