Abstract
Research in adversarial learning has primarily focused on homogeneous unstructured datasets, which often map into the problem space naturally. Inverting a feature space attack on heterogeneous datasets into the problem space is much more challenging, particularly the task of finding the perturbation to perform. This work presents a formal search strategy: the `Feature Importance Guided Attack' (FIGA), which finds perturbations in the feature space of heterogeneous tabular datasets to produce evasion attacks. We first demonstrate FIGA in the feature space and then in the problem space. FIGA assumes no prior knowledge of the defending model's learning algorithm and does not require any gradient information. FIGA assumes knowledge of the feature representation and the mean feature values of defending model's dataset. FIGA leverages feature importance rankings by perturbing the most important features of the input in the direction of the target class. While FIGA is conceptually similar to