78 citations · 111 across the 27 of their papers we have counts for
10 papers · 1 filter
Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning
Jinyin Chen, Wenbo Mu, Luxin Zhang +3
Graph neural network (GNN) has captured wide attention due to its capability of graph representation learning for graph-structured data. However, the distributed data silos limit t…
Robust Knowledge Distillation Based on Feature Variance Against Backdoored Teacher Model
Jinyin Chen, Xiaoming Zhao, Haibin Zheng +3
Benefiting from well-trained deep neural networks (DNNs), model compression have captured special attention for computing resource limited equipment, especially edge devices. Knowl…
Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs
Haibin Zheng, Haiyang Xiong, Jinyin Chen +2
Graph neural network (GNN) with a powerful representation capability has been widely applied to various areas, such as biological gene prediction, social recommendation, etc. Recen…
Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency
Jinyin Chen, Mingjun Li, Tao Liu +3
Federated learning (FL) is a distributed machine learning approach where multiple clients collaboratively train a joint model without exchanging their data. Despite FL's unpreceden…
Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons
Haibo Jin, Ruoxi Chen, Haibin Zheng +4
Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequen…
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification
Haibin Zheng, Zhiqing Chen, Tianyu Du +6
Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions espe…