> The point is that the human brain is just as vulnerable to the adversarial attacks that people are claiming will be the downfall of ML That may be the point,

> The point is that the human brain is just as vulnerable to the adversarial attacks that people are claiming will be the downfall of ML That may be the point, but it's not proven by this study. Cats and dogs have many structural similarities, as do the other adversarial examples (panda / gibbon, cabbage / broccoli). We know that this isn't just an artefact of the human visual system because we know they are very similar in non-visual ways too, i.e. they are genetically and behaviourally similar (compared with random other items in the world, such as bananas and toasters). This study's choice of images seems to acknowledge that when the human visual system makes mistakes, it does so in a far more robust way than ML-generated models do. Even the fact that this effect is robust across many individuals, versus adversarial ML images being model-specific, demonstrates this. Mistaking a cat for a dog, given a 50ms window, is much less likely to be disadvantageous to us than mistaking a cat for a computer, or a banana for a toaster. In other words, this study is a long way away from demonstrating that applying a bit of static to an image could make us mistake a car for a tree, whereas in an ML scenario such a mistake seems quite plausible.