Though there are several works about adversarial attack and defense strategies on domains such as images and natural language processing, it is still difficult to directly transfer the learned knowledge to graph
然后,作者在总结之前的工作时提到了两种基于梯度的攻击方法(但不是针对于图神经网络的),分别为: Fast Gradient Sign Method(FGSM) attack 和Jacobian−based Saliency Map Approach(JSMA) attack。 FGSM: η=ϵsign(∇Jθ(x,l)) ,其中 ϵ 表示扰动的大小, sign 获取梯度的符号。 x′=x+η 该方法的...
本文介绍相关的三篇文章:Adversarial Attacks on Neural Networks for Graph Data[1]、 Adversarial Attacks on Graph Neural Networks via Meta Learning[2]和Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective[3]。 一、图对抗攻击 由于深度神经网络强大的表示学习能力,近几年它在...
📝Arxiv'20 Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study 📝Arxiv'20 Adversarial Attacks and Defenses in Images, Graphs and Text: A Review 📝Arxiv'19 Adversarial Attack and Defense on Graph Data: A Survey 📝Arxiv'18 Deep Learning on Graphs A Survey 📝Ar...
After that, we provide comments and discussions on the effectiveness of the presented attack and defense techniques. The remainder of the paper is organized as follows: In Section 2, we first sketch out the background. In Section 3, we detail several classic adversarial attack methods. In ...
Sun, L., et al.: Adversarial attack and defense on graph data: a survey. In: CoRR abs/1812.10528 (2018).arXiv: 1812.10528 Tang, H., et al.: Adversarial attack on hierarchical graph pooling neural networks. In: arXiv preprintarXiv:2005.11560(2020) ...
2C). We found that with an increasing attack strength ɛ, the amount of visible noise on the images increased (Fig. 2D). We quantified this in a blinded observer study and found that the detection threshold for adversarial attacks was ɛ = 0.19 for ResNet models and ɛ = ...
but different model parameters and structure. Generate adversarial samples through the proxy model, which not only has a high attack output rate on the white-box model, but also shows a stronger transfer capability. As shown in Fig.4we observed that introducing an adversarial perturbation to a ...
A是攻击节点集合,即可被操纵的节点集,v0是目标节点,即希望通过攻击改变分类结果的节点。如果直接对目标节点v0进行修改,则称为直接攻击(direct attack);反之,对v0节点以外的节点进行修改,间接地影响v0的分类结果,称为影响者攻击(influencer attack)。 基于图的对抗性攻击模型可定义为下式(式1),其中Z∗是GCN分类...
这篇文章研究的内容是:通过改变图的拓扑结构,来影响分类器模型的预测结果,进而研究分类器模型到底学到了什么,并且有效提升模型的鲁棒性。 这篇文章首先提出了一种基于强化学习的攻击方法,该方法只需要分类器…