[mjn]

> outperform

I think that remains to be seen, at least in the general case, since we haven't yet agreed on a measure of performance. The debate around adversarial examples can be interpreted as arguing over the proper measure of performance. Although so far the debate is doing so somewhat implicitly, since afaik nobody has formalized a measure of robustness to adversarial examples; it's progressed more by case studies (which is fine, since research into NN robustness is still quite early stage, and case studies can help illustrate issues). I think it can be fairly said that neural nets perform well on the ImageNet benchmark and similar measures of performance. But whether those are good measures of performance, or whether some kind of metric that weights robustness more heavily should be used (and what methods would perform well on that) is the subject of current research, like this research.