Isn't that basically what an optical illusion is?

Yes, and also this: https://en.wikipedia.org/wiki/Dazzle_camouflage

Hacks human brain rather efficiently.

> Dazzle was adopted by the Admiralty in the UK, and then by the United States Navy, with little evaluation. Each ship's dazzle pattern was unique to avoid making classes of ships instantly recognisable to the enemy. The result was that a profusion of dazzle schemes was tried, and the evidence for their success was at best mixed. So many factors were involved that it was impossible to determine which were important, and whether any of the colour schemes were effective.

It is true that battlefield efficiency of such camouflage is unknown - but I think one can see the effects it does on the brain without conducting a proper rigorous study. The question here is not whether the effect exists - which is IMO obvious - but whether it's enough to make difference in actual combat.

Plus cognitive biases. Those are already well understood and used in a variety of ways.

The one-pixel attack works for pretty much any machine learning model, not just one single neural network.

500,000 years ago our eyes and vision were probably significantly worse than they are today. As our eyes evolved to capture the world better our brains also evolved to correct the errors from our eyes. On the other hand, we feed into our neural networks high quality images. It's true that they are low resolution but they don't contain noticeable noise or artifacts. The attack described here is a smart application of salt and pepper noise. It's ineffective on humans because our vision evolved to filter it out, but a network which has seen only noiseless images is helpless.

I'm curious whether training the network by adding noise and other mutations to the set would make the network more resilient to this attacks. In other words, it's the training set or the network architecture that's vulnerable here?

>I'm curious whether training the network by adding noise and other mutations to the set would make the network more resilient to this attacks. In other words, it's the training set or the network architecture that's vulnerable here?

This is called adversarial training and is currently the most popular technique for protecting neural networks against this type of attack. That being said, it doesn't work as well as one would hope: the adversarially trained models are usually still vulnerable to other attacks.