[jkldotio]

I find it less interesting in terms of applications. I am sure finding the causes of these illusions will help in understanding aspects of how the system, human or machine, subject to the illusion operates; however, so many of them depend on either a static image or a fixed perspective that it just doesn't seem to be all that big of an issue to me.

The visual illusions humans are subject to are frequently defeated by some simple motion to reposition the viewer and there seem to be few illusions that can sustain being in motion themselves and still fool people. From that it doesn't seem all that revolutionary that a fixed position view of a three dimensional reality, with no fourth dimension of time, is not omniscient. It only has one bias sample or view of the scene, so why would we expect it to be omniscient?

The paper "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"[0] is interesting in its explorations of the issue but I still don't get the hype around "fooling" DNNs. Even if someone gets an actual video scene, a timeseries of frame after frame, that still fools some kind of DNN (perhaps a LSTM => softmax classification), it's still not all that interesting as that occasionally seems to happen to humans too.

[0]http://arxiv.org/abs/1412.1897 (web, but the pdf linked from there is ~9.5MB)