  Deep neural networks are susceptible to adversarially crafted, small and
imperceptible changes in the natural inputs. The most effective defense
mechanism against these examples is adversarial training which constructs
adversarial examples during training by iterative maximization of loss. The
model is then trained to minimize the loss on these constructed examples. This
min-max optimization requires more data, larger capacity models, and additional
computing resources. It also degrades the standard generalization performance
of a model. Can we achieve robustness more efficiently? In this work, we
explore this question from the perspective of knowledge transfer. First, we
theoretically show the transferability of robustness from an adversarially
trained teacher model to a student model with the help of mixup augmentation.
Second, we propose a novel robustness transfer method called Mixup-Based
Activated Channel Maps (MixACM) Transfer. MixACM transfers robustness from a
robust teacher to a student by matching activated channel maps generated
without expensive adversarial perturbations. Finally, extensive experiments on
multiple datasets and different learning scenarios show our method can transfer
robustness while also improving generalization on natural images.
