  Raw deep neural network (DNN) performance is not enough; in real-world
settings, computational load, training efficiency and adversarial security are
just as or even more important. We propose to simultaneously tackle
Performance, Efficiency, and Robustness, using our proposed algorithm Q-TART,
Quickly Train for Adversarial Robustness and in-Transferability. Q-TART follows
the intuition that samples highly susceptible to noise strongly affect the
decision boundaries learned by DNNs, which in turn degrades their performance
and adversarial susceptibility. By identifying and removing such samples, we
demonstrate improved performance and adversarial robustness while using only a
subset of the training data. Through our experiments we highlight Q-TART's high
performance across multiple Dataset-DNN combinations, including ImageNet, and
provide insights into the complementary behavior of Q-TART alongside existing
adversarial training approaches to increase robustness by over 1.3% while using
up to 17.9% less training time.
