  Adversarial learning has emerged as one of the successful techniques to
circumvent the susceptibility of existing methods against adversarial
perturbations. However, the majority of existing defense methods are tailored
to defend against a single category of adversarial perturbation (e.g.
$\ell_\infty$-attack). In safety-critical applications, this makes these
methods extraneous as the attacker can adopt diverse adversaries to deceive the
system. Moreover, training on multiple perturbations simultaneously
significantly increases the computational overhead during training. To address
these challenges, we propose a novel meta-learning framework that explicitly
learns to generate noise to improve the model's robustness against multiple
types of attacks. Its key component is Meta Noise Generator (MNG) that outputs
optimal noise to stochastically perturb a given sample, such that it helps
lower the error on diverse adversarial perturbations. By utilizing samples
generated by MNG, we train a model by enforcing the label consistency across
multiple perturbations. We validate the robustness of models trained by our
scheme on various datasets and against a wide variety of perturbations,
demonstrating that it significantly outperforms the baselines across multiple
perturbations with a marginal computational cost.
