  Training deep neural network classifiers that are certifiably robust against
adversarial attacks is critical to ensuring the security and reliability of
AI-controlled systems. Although numerous state-of-the-art certified training
methods have been developed, they are computationally expensive and scale
poorly with respect to both dataset and network complexity. Widespread usage of
certified training is further hindered by the fact that periodic retraining is
necessary to incorporate new data and network improvements. In this paper, we
propose Certified Robustness Transfer (CRT), a general-purpose framework for
reducing the computational overhead of any certifiably robust training method
through knowledge transfer. Given a robust teacher, our framework uses a novel
training loss to transfer the teacher's robustness to the student. We provide
theoretical and empirical validation of CRT. Our experiments on CIFAR-10 show
that CRT speeds up certified robustness training by $8 \times$ on average
across three different architecture generations while achieving comparable
robustness to state-of-the-art methods. We also show that CRT can scale to
large-scale datasets like ImageNet.
