Many safety-critical applications of machine learning, such as fraud or abuse detection, use data in tabular domains. Adversarial examples can be particularly damaging for these applications. Yet, existing works on adversarial robustness primarily focus on machine-learning models in image and text domains. We argue that, due to the differences between tabular data and images or text, existing threat models are not suitable for tabular domains. These models do not capture that the costs of an attack could be more significant than imperceptibility, or that the adversary could assign different values to the utility obtained from deploying different adversarial examples. We demonstrate that, due to these differences, the attack and defense methods used for images and text cannot be directly applied to tabular settings. We address these issues by proposing new cost and utility-aware threat models that are tailored to the adversarial capabilities and constraints of attackers targeting tabular domains. We introduce a framework that enables us to design attack and defense mechanisms that result in models protected against cost and utility-aware adversaries, for example, adversaries constrained by a certain financial budget. We show that our approach is effective on three datasets corresponding to applications for which adversarial examples can have economic and social implications.
翻译:计算机学习的许多安全关键应用,如欺诈或滥用检测,使用表格域的数据。反面的例子可能对这些应用特别有害。然而,关于对抗性强力的现有工作主要侧重于图像和文本域的机器学习模型。我们争辩说,由于表格数据和图像或文字之间的差异,现有威胁模型不适合表格域。这些模型并不认为攻击的代价可能比不可感知性更严重,或者对手可能给从部署不同的对抗性实例中获得的效用分配不同的价值。我们证明,由于这些差异,图像和文本所用的攻击和防御方法不能直接应用于表格环境。我们通过提出新的成本和实用意识威胁模型来解决这些问题,这些模型适合攻击者针对表格域的对抗能力和限制。我们引入了一种框架,使我们能够设计攻击和防御机制,这些模型能够防止成本和实用意识对敌,例如对手受到某种财政预算的制约。我们表明,我们的方法对三种与对抗性例子可能具有经济和社会影响的应用程序相对应的数据集是有效的。</s>