You are, unfortunately, probably just playing out Mr. Crab's obsession with record players.

Remember that these tricky images are based on the principle that machine-learning algorithms are differentiable and high-dimensional. There is a lot of ways to transition between, say, the desktop dimension and the cat dimension, and it's all continuous, so we're guaranteed to be able to influence the machine in that sort of direction.

You could imagine somehow taking all of the adversarial examples and categorically augmenting a machine's learning to know about the examples, creating a cat-masquerading-as-desktop dimension. But all you've done is make a lot more space (by adding a dimension) and so the next iteration of adversarial examples will be able to proceed by the same process as before, just on this new augmented machine.