I'm not sure how the quote supports your argument. Adversial examples generalize well accross many different classifiers.
Shallow NN's can be fooled just as well, it seems to be more of a problem of linear models in general. Apparently Geoff Hintons Capsule Networks are more robust due to being "less linear" (Ian Goodfellow mentioned this in a recent talk, don't have the references now to back it up)
I'm not sure it's about how shallow the network is; even logistic regression can be fooled by the same techniques (e.g. 1-layer NN). That being said, maybe it does have something to do with linearity (I suspect not) or maybe it's just generally harder to deal with nonlinear functions.
Shallow NN's can be fooled just as well, it seems to be more of a problem of linear models in general. Apparently Geoff Hintons Capsule Networks are more robust due to being "less linear" (Ian Goodfellow mentioned this in a recent talk, don't have the references now to back it up)