对抗性 Botometer：面向社交机器人检测的对抗性分析
Adversarial Botometer: Adversarial Analysis for Social Bot Detection
AI总结：
本文在对抗博弈、数据投毒和跨领域三种场景下系统评估文本社交机器人检测器的鲁棒性，并基于两个基准数据集揭示不同类别机器人的检测表现。
摘要：
社交机器人在许多在线社交网络（OSN）中扮演着重要角色，因为它们模仿人类行为。这一事实引发了关于其能力和潜在风险的难题。鉴于生成式人工智能（GenAI）的最新进展，社交机器人能够生成高度逼真且复杂的内容，模仿人类的创造力。随着恶意社交机器人利用其不真实的内容欺骗人们，识别它们并区分其生成的内容已成为众多社交平台面临的实际挑战。文献中已经提出了若干解决该问题的方法，但所提出的解决方案尚未得到广泛评估。为了解决这一问题，我们在一个竞争环境中评估基于文本的机器人检测器的行为，并提出若干场景：第一，考察机器人与机器人检测器之间的拉锯战。分析哪一方更有可能获胜以及哪些情况会影响这些预期是很有意义的。为此，我们将该问题建模为一个合成对抗博弈，其中对话机器人和机器人检测器参与策略性的在线交互。第二，在社交机器人生成的攻击样本下评估机器人检测模型；为此，我们用攻击样本污染数据集，并在该条件下评估模型性能。最后，为了研究数据集的影响，进行了跨领域分析。通过使用两个基准数据集对不同类别的社交机器人进行全面评估，我们能够展示一些可供未来工作利用的成果。
英文摘要：
Social bots play a significant role in many online social networks (OSN) as they imitate human behavior. This fact raises difficult questions about their capabilities and potential risks. Given the recent advances in Generative AI (GenAI), social bots are capable of producing highly realistic and complex content that mimics human creativity. As the malicious social bots emerge to deceive people with their unrealistic content, identifying them and distinguishing the content they produce has become an actual challenge for numerous social platforms. Several approaches to this problem have already been proposed in the literature, but the proposed solutions have not been widely evaluated. To address this issue, we evaluate the behavior of a text-based bot detector in a competitive environment where some scenarios are proposed: \textit{First}, the tug-of-war between a bot and a bot detector is examined. It is interesting to analyze which party is more likely to prevail and which circumstances influence these expectations. In this regard, we model the problem as a synthetic adversarial game in which a conversational bot and a bot detector are engaged in strategic online interactions. \textit{Second}, the bot detection model is evaluated under attack examples generated by a social bot; to this end, we poison the dataset with attack examples and evaluate the model performance under this condition. \textit{Finally}, to investigate the impact of the dataset, a cross-domain analysis is performed. Through our comprehensive evaluation of different categories of social bots using two benchmark datasets, we were able to demonstrate some achivement that could be utilized in future works.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
