Black-box Graph Adversarial Attacks Based on Topology and Feature Fusion
In the era of big data,the close relationship between data is widespread,graph data analysis and mining have become an important development trend of big data technology.In recent years,as a novel type of graph representation learning tool,graph neural networks(GNNs)have extensively attracted academic and industry attention.At present,GNNs have achieved great suc-cess in various real-world applications.Lately,many researchers believe that the security and confidence level of artificial intelli-gence is a vital point,a lot of work focuses on deep learning adversarial attacks on Euclidean structure data such as images now.This paper mainly focuses on the black-box adversarial attack problem of graph data,which is a typical non-European structure.When the graph neural network model information(structure and parameters)is unknown,the imperceptible non-random pertur-bation of graph data is carried out to realize the adversarial attack on the model,and the performance of the model decreases.Ap-plying an imperceptible no-random perturbation to the graph structure or node attributes can easily fool GNNs.The method based on node-selected black-box adversarial attack is vital,but similar methods are only taking account of the topology information of nodes instead of fully considering the information of node features,so in this paper,we propose a black-box adversarial attack for graph neural network via topology and feature fusion on citation network.In the process of selecting important nodes,this method fuses the features information and topology information of graph nodes,so that the selected nodes are significant to the graph data in both features and topology.Attackers apply small perturbations on node attributes that nodes are selected by our method and this attack has a great impact on the model.Moreover,experiments on three classic datasets show that the proposed attack strate-gy can remarkably reduce the performance of the model without access to model parameters and is better than the baseline methods.
NETL
NSTL
万方数据
