67 citations · 109 across the 14 of their papers we have counts for
5 papers · 1 filter
IDEA: Invariant Defense for Graph Adversarial Robustness
Shuchang Tao, Qi Cao, Huawei Shen +3
Despite the success of graph neural networks (GNNs), their vulnerability to adversarial attacks poses tremendous challenges for practical applications. Existing defense methods suf…
Graph Adversarial Immunization for Certifiable Robustness
Shuchang Tao, Huawei Shen, Qi Cao +3
Despite achieving great success, graph neural networks (GNNs) are vulnerable to adversarial attacks. Existing defenses focus on developing adversarial training or model modificatio…
Adversarial Camouflage for Node Injection Attack on Graphs
Shuchang Tao, Qi Cao, Huawei Shen +4
Node injection attacks on Graph Neural Networks (GNNs) have received increasing attention recently, due to their ability to degrade GNN performance with high attack success rates.…
Single Node Injection Attack against Graph Neural Networks
Shuchang Tao, Qi Cao, Huawei Shen +3
Node injection attack on Graph Neural Networks (GNNs) is an emerging and practical attack scenario that the attacker injects malicious nodes rather than modifying original nodes or…
Adversarial Immunization for Certifiable Robustness on Graphs
Shuchang Tao, Huawei Shen, Qi Cao +2
Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning…