Wild Patterns Surveyed：机器学习安全性对训练数据中毒的调查
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning
摘要：
机器学习的成功得益于计算能力和大型训练数据集的日益可用性。训练数据用于学习新模型或更新现有模型，假设它充分代表了测试时将遇到的数据。这种假设受到了中毒威胁的挑战，中毒是一种在测试时操纵训练数据以损害模型性能的攻击。虽然中毒已被公认为工业应用中的相关威胁，并且到目前为止已经提出了各种不同的攻击和防御，但仍然缺少对该领域的完整系统化和批判性审查。在这项调查中，我们提供了机器学习中中毒攻击和防御的全面系统化，回顾了过去15年来在该领域发表的100多篇论文。我们首先对当前的威胁模型和攻击进行分类，然后相应地组织现有的防御。虽然我们主要关注计算机视觉应用，但我们认为我们的系统化也包括对其他数据模式的最先进的攻击和防御。最后，我们讨论了中毒研究的现有资源，并阐明了目前的局限性和开放的研究问题，在这一研究领域。
英文摘要：
The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that will be encountered at test time. This assumption is challenged by the threat of poisoning, an attack that manipulates the training data to compromise the model's performance at test time. Although poisoning has been acknowledged as a relevant threat in industry applications, and a variety of different attacks and defenses have been proposed so far, a complete systematization and critical review of the field is still missing. In this survey, we provide a comprehensive systematization of poisoning attacks and defenses in machine learning, reviewing more than 100 papers published in the field in the last 15 years. We start by categorizing the current threat models and attacks, and then organize existing defenses accordingly. While we focus mostly on computer-vision applications, we argue that our systematization also encompasses state-of-the-art attacks and defenses for other data modalities. Finally, we discuss existing resources for research in poisoning, and shed light on the current limitations and open research questions in this research field.
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。
