It's interesting how quickly support vector machines went from the hot new thing to classify images to an afterthought after deep learning started having great results.
The same thing happened with data storage. As soon as big data appeared everyone stopped doing just data and started doing "big data". Now the term is kind of a joke even.
I predict in a few years "deep learning" term will become mostly used in an ironic sense as well.
I may be a bit behind the times, but I'm also mystified by "deep learning's" popularity. Both giant neural nets and kernel methods have overfitting problems: torture a billion-parameter model long enough, and it will tell you what you want to hear.
SVMs address this by finding a large margin for error, which will hopefully improve generalization. DNNs (I think) do this by throwing more ("big") data at the problem and hoping that the training set covers all possible inputs. Work on adversarial learning suggests that DNNs go completely off the rails when presented with anything slightly unexpected.
Deep learning gets a lot of hype because for many applications they perform better and scale better without a lot of tricks and extensions which are now possible with SVMs. You can even use a large margin loss with deep models to get some of the benefits of SVMs.
Adversarial examples are way overblown. First, SVMs are not immune to them either. Second, very few applications are threatened by things like adversarial examples.
I don't think your scenario is likely to occur unless something else starts outperforming deep learning (in the broadest sense) _and_ there's an approachable alternative to solve the same problems at least as well.
SVMs have been in active use since the early nineties and have been formulated much before that