Adversarial Attacks on Neural Networks for Graph Data 1. 背景 基于图的深度学习模型,尤其是图卷积网络,在节点分类任务上取得了很好的性能,但是此前还没有人研究过他们对于对抗性攻击是否具有鲁棒性。然而,对抗性攻击在一些实际应用场景中是十分常见的,如:搜索引擎、推荐系统、欺诈检测等。攻击者可能通过修改自身属...
图神经网络论文笔记1:Adversarial Attacks on Neural Networks for Graph Data(Nettack) Abner OOD / LLM / 3DV / 25Fall12 人赞同了该文章 这篇文章提出了一种针对节点分类的攻击,属于灰盒针对性攻击,通过操纵图结构特征和节点特征,同时通过保留重要的数据特征例如(度分布,特征的共现),确保攻击不易被察觉。作者...
In the current chapter, we overview the existing researches on graph adversarial attacks. In particular, we briefly summarize and classify the existing graph adversarial attack methods, e.g., heuristic, gradient and reinforcement learning, and then choose several classic adversarial attack methods on ...
此外,本文采取的attack是可迁移的,学习出来的attack可以在只提供一点graph信息的情况下成功的对其他的node classification model进行成功的攻击。 introduction第一段先介绍graph data有多普遍,有多重要,然后引出graph data上最常用的模型之一就是节点分类的模型并对此模型简介。第二段过渡,指出近些年graph上深度学习的模型...
系统标签: adversarial graph attack structured data classifier AdversarialAttackonGraphStructuredData HanjunDai 1 HuiLi 2 TianTian 3 XinHuang 2 LinWang 2 JunZhu 3 LeSong 12 Abstract Deeplearningongraphstructureshasshownexcit- ingresultsinvariousapplications.However,few attentionshavebeenpaidtotherobustnessof...
classification的准确率即使在采用很少的一点perturbation的情况下也会明显下降。此外,本文采取的attack是可迁移的,学习出来的attack可以在只提供一点graph信息的情况下成功的对其他的node classification model进行成功的攻击。 introduction第一段先介绍graph data有多普遍,有多重要,然后引出graph ...
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Deep learning on graph structures has shown exciting results in various applications. However, few attentions have been paid to the robustness of such models, in contrast to numerous research work for image or text adversarial attack and defense. In this paper, we focus on the adversarial attacks...
In addition to attacks at test time, we tackle the more challenging class of poisoning/causative attacks, which focus on the training phase of a machine learning model. We generate adversarial perturbations targeting the node's features and the graph structure, thus, taking the dependencies between...
四、attack method 4.0 问题定义 先说明一下用到的符号定义与一些名词定义: 攻击目的: target node:我们希望攻击的点,攻击成功的标志是该节点被分错类。 attacker node:很多时候我们并不能直接修改到target node,而是通过我们能修改的一些其他节点,间接影响对于target node的分类。