量化图对抗攻击中结构扰动的噪声
Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks
AI总结：
本文提出噪声概念量化图对抗攻击中每个对抗链接的攻击强度，并基于噪声和分类间隔设计了三种单步与多步攻击策略，实验证明其有效性并分析了有效扰动的偏好模式。
摘要：
图神经网络因其在利用邻居局部信息方面具有强大的学习能力，已被广泛应用于解决图相关任务。然而，近期关于图对抗攻击的研究表明，当前的图神经网络对恶意攻击并不鲁棒。然而，现有的许多工作主要集中在基于攻击性能的优化目标上，以获得（接近）最优的扰动，却较少关注每次扰动（例如注入特定节点/链接）的强度量化，这使得扰动选择成为一个缺乏可解释性的黑盒模型。在本研究中，我们提出了噪声的概念，以量化每个对抗链接的攻击强度。此外，基于所定义的噪声和分类间隔，我们针对单步和多步优化提出了三种攻击策略。在基准数据集上针对三种代表性图神经网络进行的大量实验证明了所提攻击策略的有效性。特别地，我们还通过分析所选扰动节点的相应属性，探讨了有效对抗扰动的偏好模式。
英文摘要：
Graph neural networks have been widely utilized to solve graph-related tasks because of their strong learning power in utilizing the local information of neighbors. However, recent studies on graph adversarial attacks have proven that current graph neural networks are not robust against malicious attacks. Yet much of the existing work has focused on the optimization objective based on attack performance to obtain (near) optimal perturbations, but paid less attention to the strength quantification of each perturbation such as the injection of a particular node/link, which makes the choice of perturbations a black-box model that lacks interpretability. In this work, we propose the concept of noise to quantify the attack strength of each adversarial link. Furthermore, we propose three attack strategies based on the defined noise and classification margins in terms of single and multiple steps optimization. Extensive experiments conducted on benchmark datasets against three representative graph neural networks demonstrate the effectiveness of the proposed attack strategies. Particularly, we also investigate the preferred patterns of effective adversarial perturbations by analyzing the corresponding properties of the selected perturbation nodes.
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