32 citations · 40 across the 6 of their papers we have counts for
6 papers
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…
Edge Deep Learning Model Protection via Neuron Authorization
Jinyin Chen, Haibin Zheng, Tao Liu +4
With the development of deep learning processors and accelerators, deep learning models have been widely deployed on edge devices as part of the Internet of Things. Edge device mod…
Understanding Real-world Threats to Deep Learning Models in Android Apps
Zizhuang Deng, Kai Chen, Guozhu Meng +3
Famous for its superior performance, deep learning (DL) has been popularly used within many applications, which also at the same time attracts various threats to the models. One pr…
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…