  Adversarial training is a defense technique that improves adversarial
robustness of a deep neural network (DNN) by including adversarial examples in
the training data. In this paper, we identify an overlooked problem of
adversarial training in that these adversarial examples often have different
semantics than the original data, introducing unintended biases into the model.
We hypothesize that such non-semantics-preserving (and resultingly ambiguous)
adversarial data harm the robustness of the target models. To mitigate such
unintended semantic changes of adversarial examples, we propose
semantics-preserving adversarial training (SPAT) which encourages perturbation
on the pixels that are shared among all classes when generating adversarial
examples in the training stage. Experiment results show that SPAT improves
adversarial robustness and achieves state-of-the-art results in CIFAR-10 and
CIFAR-100.
