This technique exploits the fact that the CNN is completely deterministic - see my reply above. It may be very difficult for stochastic networks.

CNNs are comparable to the first 150ms or so of human vision, before feedback , multiple saccades, and higher order mental programs kicks in. So the difficulty in generating these fooling images also depends on the complexity of the inference - a more complex AGI with human-like vision given larger amounts of time to solve the task would probably also be harder to fool, independent of the stochasticity issue.