Title: Imperceptible Adversarial Attacks on Tabular Data Show Chinese title

Title: 表格数据上的不可察觉对抗攻击

Abstract: Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions. While research on the topic has mainly been focusing on the image domain, numerous industrial applications, in particular in finance, rely on standard tabular data. In this paper, we discuss the notion of adversarial examples in the tabular domain. We propose a formalization based on the imperceptibility of attacks in the tabular domain leading to an approach to generate imperceptible adversarial examples. Experiments show that we can generate imperceptible adversarial examples with a high fooling rate. Show Chinese abstract

Abstract: 机器学习模型的安全性是一个值得关注的问题，因为它们可能面临对抗攻击，从而做出不必要的有利决策。 尽管该领域的研究主要集中在图像领域，但许多工业应用，特别是在金融领域，依赖于标准的表格数据。 在本文中，我们讨论了表格领域中的对抗样本概念。 我们提出了一种基于表格领域中攻击不可察觉性的形式化方法，从而提出了一种生成不可察觉的对抗样本的方法。 实验表明，我们可以以高欺骗率生成不可察觉的对抗样本。