  Adversarial training is an approach of increasing the robustness of models to
adversarial attacks by including adversarial examples in the training set. One
major challenge of producing adversarial examples is to contain sufficient
perturbation in the example to flip the model's output while not making severe
changes in the example's semantical content. Exuberant change in the semantical
content could also change the true label of the example. Adding such examples
to the training set results in adverse effects. In this paper, we present the
Calibrated Adversarial Training, a method that reduces the adverse effects of
semantic perturbations in adversarial training. The method produces pixel-level
adaptations to the perturbations based on novel calibrated robust error. We
provide theoretical analysis on the calibrated robust error and derive an upper
bound for it. Our empirical results show a superior performance of the
Calibrated Adversarial Training over a number of public datasets.
