Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks

09/22/2020
by   Boyuan Feng, et al.
0

With the increasing popularity of graph-based learning, graph neural networks (GNNs) emerge as the essential tool for gaining insights from graphs. However, unlike the conventional CNNs that have been extensively explored and exhaustively tested, people are still worrying about the GNNs' robustness under the critical settings, such as financial services. The main reason is that existing GNNs usually serve as a black-box in predicting and do not provide the uncertainty on the predictions. On the other side, the recent advancement of Bayesian deep learning on CNNs has demonstrated its success of quantifying and explaining such uncertainties to fortify CNN models. Motivated by these observations, we propose UAG, the first systematic solution to defend adversarial attacks on GNNs through identifying and exploiting hierarchical uncertainties in GNNs. UAG develops a Bayesian Uncertainty Technique (BUT) to explicitly capture uncertainties in GNNs and further employs an Uncertainty-aware Attention Technique (UAT) to defend adversarial attacks on GNNs. Intensive experiments show that our proposed defense approach outperforms the state-of-the-art solutions by a significant margin.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

09/28/2020

RoGAT: a robust GNN combined revised GAT with adjusted graphs

Graph Neural Networks(GNNs) are useful deep learning models to deal with...
04/30/2021

Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense

Graph Neural Networks (GNNs) have received significant attention due to ...
10/26/2021

Robustness of Graph Neural Networks at Scale

Graph Neural Networks (GNNs) are increasingly important given their popu...
07/19/2020

Adversarial Immunization for Improving Certifiable Robustness on Graphs

Despite achieving strong performance in the semi-supervised node classif...
09/28/2021

DEBOSH: Deep Bayesian Shape Optimization

Shape optimization is at the heart of many industrial applications, such...
06/09/2020

Black-Box Adversarial Attacks on Graph Neural Networks with Limited Node Access

We study the black-box attacks on graph neural networks (GNNs) under a n...
03/02/2020

Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study

Deep neural networks (DNNs) have achieved significant performance in var...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.