For all we know, humans have similar problems on some obscure subset of images, but we can't find human's adversarial examples because we don't have detailed knowledge of how the brain processes images.
In these discussions, someone always mentions optical illusions, but only humans (so far) understand the concept of 'optical illusion', and recognize that they are experiencing them.
This is true, but step one is "move your head" (or in your words, "get a better view" -- but you get more value from just the fact that your head is in a different place than from the possibility of a better angle on whatever you're looking at).
That strategy doesn't work at all when you're trying to classify static images rather than physical objects.
And moving one's head to get a a better view is only one thing that a human might do. Firstly, of course, we must recognize that we are having a difficulty, and current machine vision seems to be somewhat deficient in this regard. Then, even without being able to get a different perspective, we will do things like make guesses as to what might be there (using our extensive semantic models of the world) and figure out if they might be a good fit to what we see, and/or we might try to extract specific features of the problematic area and search our memories for objects that might plausibly match, bearing in mind that it might be from a different perspective than we are accustomed to. We are also quite good at estimating whether an object might be a problem for us, even if we have not positively identified it. There is a lot more to it than just moving one's head.
Some more subtle examples:
http://www.terrycolon.com/1features/optical-illusions.html
In fact human perceptual processes are only kind of reliable some of the time. Low and/or unusual light, suggestibility, and unusual contexts all have a very negative effect on reliability, but humans are often unaware of this.
Cognitive and semantic illusions are even more persistent. People literally believe all kinds of nonsense, and will carry on believing it even when offered robust evidence that they're wrong.
The point being that human perception and cognition are not some kind of gold standard. They have plenty of issues of their own. But there's a kind of assumption/requirement of perfection with machine intelligence that doesn't apply to human cognition. So bugs in our own evolutionary firmware tend to be overlooked, while equivalent-level bugs in ML are seen as terrible failures which undermine the entire premise of AI.
Based on what?
There are plenty of those, and I personally I think they're probably analogous to how adversarial filters fool AI classifiers.
https://www.dw.com/en/man-falls-into-black-hole-art-exhibit-...
(a) do not occur frequently in nature,
(b) are not frequently - if at all - produced in man-made architecture or transit-constructions,
(c) often contain repetitive and regular geometric and chromatic patterns which further make them stand out from everything else, and
(d) practically cannot be produced by digital (ergo noisy/less-than-perfect) images of any common real-world scenario.
In short: optical illusions don't accidentally occur in places where they can be seen by meatbag drivers.
Also a) and b) empirically seem to be true of the test sets people have collected thus far of the natural world for these models.
In short, we have no evidence that adversarial examples of the type being studied occur commonly in images collected by self driving cars.
You hypothesize that there are comparable examples for humans somewhere out there in the domain of all possible images, but the fact that, for all the countless cases of people looking at things that have occurred in humanity's existence, no-one has found a good example, suggests that, from the pragmatic point of view that you propose, image-recognition software has some catching-up to do.
Maybe a system that seeks consensus among several differently-trained models would be more robust.
Even if you could, the result would be specific to that particular person, so it won't work as good on others. And these bastards learn while you're constructing the example (which isn't fair at all to a helpless classifier that's just sitting there and doesn't change).
Looks like we are starting to find examples.
I think your intuition is wrong because humans are adapted to what exists naturally so of course there are no naturally occurring adversarial examples. It seems like the same is true for models trained on large natural image sets though.
My point is not wow let’s stop developing neural networks they are perfect. It’s more let’s go collect real world test sets to find and then fix gaps. Adversarial examples actually help very little in making nets more robust in the ways that matter.
> (a) do not occur frequently in nature
You've never heard of walking sticks? Ever seen one of those leaf moths?
If you had seen one, would you realize you had?
Is a deer visible on that stretch of hillside, or is that just dead grass?