论文标题
针对数据中毒的机器学习安全：我们到了吗？
Machine Learning Security against Data Poisoning: Are We There Yet?
论文作者
论文摘要
在许多不同应用程序中，计算能力和大量数据的可用性增加，机器学习（ML）的最新成功助长了。但是，当对此类数据进行恶意操纵以误导学习过程时，可能会损害所得模型的可信赖性。在本文中，我们首先回顾了损害用于学习ML模型的训练数据的中毒攻击，包括旨在降低整体性能，操纵对特定测试样本的预测甚至在模型中植入后门的攻击。然后，我们讨论如何使用基本的安全原理或部署面向ML的防御机制来减轻这些攻击。我们通过制定一些相关的开放挑战来结束我们的文章，这些挑战阻碍了用于评估和提高ML模型的可信度不适合数据中毒攻击的测试方法和基准的开发
The recent success of machine learning (ML) has been fueled by the increasing availability of computing power and large amounts of data in many different applications. However, the trustworthiness of the resulting models can be compromised when such data is maliciously manipulated to mislead the learning process. In this article, we first review poisoning attacks that compromise the training data used to learn ML models, including attacks that aim to reduce the overall performance, manipulate the predictions on specific test samples, and even implant backdoors in the model. We then discuss how to mitigate these attacks using basic security principles, or by deploying ML-oriented defensive mechanisms. We conclude our article by formulating some relevant open challenges which are hindering the development of testing methods and benchmarks suitable for assessing and improving the trustworthiness of ML models against data poisoning attacks
