  Adversarial training is so far the most effective strategy in defending
against adversarial examples. However, it suffers from high computational costs
due to the iterative adversarial attacks in each training step. Recent studies
show that it is possible to achieve fast Adversarial Training by performing a
single-step attack with random initialization. However, such an approach still
lags behind state-of-the-art adversarial training algorithms on both stability
and model robustness. In this work, we develop a new understanding towards Fast
Adversarial Training, by viewing random initialization as performing randomized
smoothing for better optimization of the inner maximization problem. Following
this new perspective, we also propose a new initialization strategy, backward
smoothing, to further improve the stability and model robustness over
single-step robust training methods. Experiments on multiple benchmarks
demonstrate that our method achieves similar model robustness as the original
TRADES method while using much less training time ($\sim$3x improvement with
the same training schedule).
