  In the field of adversarial robustness, there is a common practice that
adopts the single-step adversarial training for quickly developing
adversarially robust models. However, the single-step adversarial training is
most likely to cause catastrophic overfitting, as after a few training epochs
it will be hard to generate strong adversarial examples to continuously boost
the adversarial robustness. In this work, we aim to avoid the catastrophic
overfitting by introducing multi-step adversarial examples during the
single-step adversarial training. Then, to balance the large training overhead
of generating multi-step adversarial examples, we propose a Multi-stage
Optimization based Adversarial Training (MOAT) method that periodically trains
the model on mixed benign examples, single-step adversarial examples, and
multi-step adversarial examples stage by stage. In this way, the overall
training overhead is reduced significantly, meanwhile, the model could avoid
catastrophic overfitting. Extensive experiments on CIFAR-10 and CIFAR-100
datasets demonstrate that under similar amount of training overhead, the
proposed MOAT exhibits better robustness than either single-step or multi-step
adversarial training methods.
