58 citations · 96 across the 7 of their papers we have counts for
6 papers · 1 filter
Double Sampling Randomized Smoothing
Linyi Li, Jiawei Zhang, Tao Xie +1
Neural networks (NNs) are known to be vulnerable against adversarial perturbations, and thus there is a line of work aiming to provide robustness certification for NNs, such as ran…
On the Certified Robustness for Ensemble Models and Beyond
Zhuolin Yang, Linyi Li, Xiaojun Xu +3
Recent studies show that deep neural networks (DNN) are vulnerable to adversarial examples, which aim to mislead DNNs by adding perturbations with small magnitude. To defend agains…
SoK: Certified Robustness for Deep Neural Networks
Linyi Li, Tao Xie, Bo Li
Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to adve…
Adversarial Attack on Large Scale Graph
Jintang Li, Tao Xie, Liang Chen +3
Recent studies have shown that graph neural networks (GNNs) are vulnerable against perturbations due to lack of robustness and can therefore be easily fooled. Currently, most works…
A Survey of Adversarial Learning on Graphs
Liang Chen, Jintang Li, Jiaying Peng +6
Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction, and graph clustering. However, they…
TSS: Transformation-Specific Smoothing for Robustness Certification
Linyi Li, Maurice Weber, Xiaojun Xu +5
As machine learning (ML) systems become pervasive, safeguarding their security is critical. However, recently it has been demonstrated that motivated adversaries are able to mislea…