  Some recent works revealed that deep neural networks (DNNs) are vulnerable to
so-called adversarial attacks where input examples are intentionally perturbed
to fool DNNs. In this work, we revisit the DNN training process that includes
adversarial examples into the training dataset so as to improve DNN's
resilience to adversarial attacks, namely, adversarial training. Our
experiments show that different adversarial strengths, i.e., perturbation
levels of adversarial examples, have different working zones to resist the
attack. Based on the observation, we propose a multi-strength adversarial
training method (MAT) that combines the adversarial training examples with
different adversarial strengths to defend adversarial attacks. Two training
structures - mixed MAT and parallel MAT - are developed to facilitate the
tradeoffs between training time and memory occupation. Our results show that
MAT can substantially minimize the accuracy degradation of deep learning
systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.
