  Adversarial training shows promise as an approach for training models that
are robust towards adversarial perturbation. In this paper, we explore some of
the practical challenges of adversarial training. We present a sensitivity
analysis that illustrates that the effectiveness of adversarial training hinges
on the settings of a few salient hyperparameters. We show that the robustness
surface that emerges across these salient parameters can be surprisingly
complex and that therefore no effective one-size-fits-all parameter settings
exist. We then demonstrate that we can use the same salient hyperparameters as
tuning knob to navigate the tension that can arise between robustness and
accuracy. Based on these findings, we present a practical approach that
leverages hyperparameter optimization techniques for tuning adversarial
training to maximize robustness while keeping the loss in accuracy within a
defined budget.
