  Although deep networks achieve strong accuracy on a range of computer vision
benchmarks, they remain vulnerable to adversarial attacks, where imperceptible
input perturbations fool the network. We present both theoretical and empirical
analyses that connect the adversarial robustness of a model to the number of
tasks that it is trained on. Experiments on two datasets show that attack
difficulty increases as the number of target tasks increase. Moreover, our
results suggest that when models are trained on multiple tasks at once, they
become more robust to adversarial attacks on individual tasks. While
adversarial defense remains an open challenge, our results suggest that deep
networks are vulnerable partly because they are trained on too few tasks.
