Let's not forget that the word "imperceptible" is a heavily laden term in this context. There are numerous modifications to the data that would be "imperceptible" to a machine learning system, but would completely confuse a human. For example if you were to divide the image into a grid, and shuffle the squares, many ML systems would be tolerant to this kind of modification because some training regimes do this anyway. To that system you haven't changed anything important about the image and it would correctly classify it.
What this result says to me is that there are really useful features of the data that can successfully classify images that humans are totally unaware of! And that's neat.
In the linked paper [0], they actually tested that point: after applying Gaussian noise to samples, the model could still recognize them half the time[1], despite being nearly unreadable (to me).
What this result says to me is that there are really useful features of the data that can successfully classify images that humans are totally unaware of! And that's neat.