Deep generative models as an adversarial attack strategy for tabular machine learning

Deep Generative Models (DGMs) have found application in computer vision for
generating adversarial examples to test the robustness of machine learning (ML)
systems. Extending these adversarial techniques to tabular ML presents unique
challenges due to the distinct nature of tabular data and the necessity to
preserve domain constraints in adversarial examples. In this paper, we adapt
four popular tabular DGMs into adversarial DGMs (AdvDGMs) and evaluate their
effectiveness in generating realistic adversarial examples that conform to
domain constraints.