  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.
