标题： DDoD：人类-人工智能团队上的双重决策否认攻击 显示英文标题

标题： DDoD: Dual Denial of Decision Attacks on Human-AI Teams

摘要： 人工智能（AI）系统已被越来越多地用于使决策过程更快、更准确和更高效。 然而，这些系统也一直面临被攻击的持续风险。 虽然针对基于AI的应用程序的大多数攻击旨在操纵分类器或训练数据并改变AI模型的输出，但最近提出的海绵攻击旨在通过消耗大量资源来阻碍分类器的执行。 在本工作中，我们提出\textit{双重否定决策攻击 针对协作人机团队的DDoD攻击}。 我们讨论此类攻击如何耗尽\textit{计算和人工}资源，并显著损害决策能力。 我们描述了DDoD对人力和计算资源的影响，并在一系列示例领域中展示了潜在的风险场景。 显示英文摘要

摘要： Artificial Intelligence (AI) systems have been increasingly used to make decision-making processes faster, more accurate, and more efficient. However, such systems are also at constant risk of being attacked. While the majority of attacks targeting AI-based applications aim to manipulate classifiers or training data and alter the output of an AI model, recently proposed Sponge Attacks against AI models aim to impede the classifier's execution by consuming substantial resources. In this work, we propose \textit{Dual Denial of Decision (DDoD) attacks against collaborative Human-AI teams}. We discuss how such attacks aim to deplete \textit{both computational and human} resources, and significantly impair decision-making capabilities. We describe DDoD on human and computational resources and present potential risk scenarios in a series of exemplary domains.