Derivative-free optimization adversarial attacks for graph convolutional networks.

ABSTRACT: In recent years, graph convolutional networks (GCNs) have emerged rapidly due to their excellent performance in graph data processing. However, recent researches show that GCNs are vulnerable to adversarial attacks. An attacker can maliciously modify edges or nodes of the graph to mislead the model's classification of the target nodes, or even cause a degradation of the model's overall classification performance. In this paper, we first propose a black-box adversarial attack framework based on derivative-free optimization (DFO) to generate graph adversarial examples without using gradient and apply advanced DFO algorithms conveniently. Second, we implement a direct attack algorithm (DFDA) using the Nevergrad library based on the framework. Additionally, we overcome the problem of large sea

SUBMITTER: Yang R 

PROVIDER: S-EPMC8409335 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature