7 citations · 14 across the 8 of their papers we have counts for
8 papers
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network
Jinyu Cai, Yi Han, Wenzhong Guo +1
In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are…
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
Mingyuan Fan, Cen Chen, Chengyu Wang +2
Recent works have brought attention to the vulnerability of Federated Learning (FL) systems to gradient leakage attacks. Such attacks exploit clients' uploaded gradients to reconst…
Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning
Zhaoliang Chen, Lele Fu, Jie Yao +3
In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms. However, given multi-view da…
Federated Learning based on Defending Against Data Poisoning Attacks in IoT
Jiayin Li, Wenzhong Guo, Xingshuo Han +2
The rapidly expanding number of Internet of Things (IoT) devices is generating huge quantities of data, but the data privacy and security exposure in IoT devices, especially in the…
Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons
Mingyuan Fan, Yang Liu, Cen Chen +2
The opacity of neural networks leads their vulnerability to backdoor attacks, where hidden attention of infected neurons is triggered to override normal predictions to the attacker…
Wasserstein Adversarial Learning based Temporal Knowledge Graph Embedding
Yuanfei Dai, Wenzhong Guo, Carsten Eickhoff
Research on knowledge graph embedding (KGE) has emerged as an active field in which most existing KGE approaches mainly focus on static structural data and ignore the influence of…
Case-Aware Adversarial Training
Mingyuan Fan, Yang Liu, Cen Chen
The neural network (NN) becomes one of the most heated type of models in various signal processing applications. However, NNs are extremely vulnerable to adversarial examples (AEs)…
Enhance transferability of adversarial examples with model architecture
Mingyuan Fan, Wenzhong Guo, Shengxing Yu +2
Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model…