Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the construction details are unknown for attackers in a black-box scenario, it is unrealistic to conduct the classical adversarial attacks that require a specific adjacency matrix. In this paper, we propose the first general black-box adversarial attack framework, i.e., General Attack via Fake Social Interaction (GAFSI), against detectors based on different graph structures. Specifically, as sharing is an important social interaction for GNN-based fake news detectors to construct the graph, we simulate sharing behaviors to fool the detectors. Firstly, we propose a fraudster selection module to select engaged users leveraging local and global information. In addition, a post injection module guides the selected users to create shared relations by sending posts. The sharing records will be added to the social context, leading to a general attack against different detectors. Experimental results on empirical datasets demonstrate the effectiveness of GAFSI.

翻译：基于图神经网络（GNN）的假新闻检测器采用多种方法构建图，旨在学习用于分类的独特新闻嵌入表示。由于在黑盒场景中攻击者无法获知图的构建细节，因此执行依赖特定邻接矩阵的经典对抗攻击并不现实。本文提出了首个通用黑盒对抗攻击框架，即通过虚假社交交互进行通用攻击（GAFSI），该框架可针对基于不同图结构的检测器。具体而言，由于分享行为是基于GNN的假新闻检测器构建图的重要社交互动，我们通过模拟分享行为来欺骗检测器。首先，我们提出欺诈者选择模块，利用局部和全局信息选择受操纵用户。此外，帖子注入模块引导所选用户通过发布帖子创建分享关系。这些分享记录将被添加至社交上下文，从而实现对不同检测器的通用攻击。在经验数据集上的实验结果验证了GAFSI的有效性。