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

04/30/2021
by   Haoxi Zhan, et al.
0

Graph Neural Networks (GNNs) have received significant attention due to their state-of-the-art performance on various graph representation learning tasks. However, recent studies reveal that GNNs are vulnerable to adversarial attacks, i.e. an attacker is able to fool the GNNs by perturbing the graph structure or node features deliberately. While being able to successfully decrease the performance of GNNs, most existing attacking algorithms require access to either the model parameters or the training data, which is not practical in the real world. In this paper, we develop deeper insights into the Mettack algorithm, which is a representative grey-box attacking method, and then we propose a gradient-based black-box attacking algorithm. Firstly, we show that the Mettack algorithm will perturb the edges unevenly, thus the attack will be highly dependent on a specific training set. As a result, a simple yet useful strategy to defense against Mettack is to train the GNN with the validation set. Secondly, to overcome the drawbacks, we propose the Black-Box Gradient Attack (BBGA) algorithm. Extensive experiments demonstrate that out proposed method is able to achieve stable attack performance without accessing the training sets of the GNNs. Further results shows that our proposed method is also applicable when attacking against various defense methods.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

08/21/2021

A Hard Label Black-box Adversarial Attack Against Graph Neural Networks

Graph Neural Networks (GNNs) have achieved state-of-the-art performance ...
08/04/2019

The General Black-box Attack Method for Graph Neural Networks

With the great success of Graph Neural Networks (GNNs) towards represent...
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...
09/22/2020

Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks

With the increasing popularity of graph-based learning, graph neural net...
12/16/2019

Adversarial Model Extraction on Graph Neural Networks

Along with the advent of deep neural networks came various methods of ex...
07/15/2021

Algorithmic Concept-based Explainable Reasoning

Recent research on graph neural network (GNN) models successfully applie...
03/22/2022

Exploring High-Order Structure for Robust Graph Structure Learning

Recent studies show that Graph Neural Networks (GNNs) are vulnerable to ...
This week in AI

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