While I was reading a blog post, I came across with an explanation saying that "ϵ you need to consider for $ℓ^2$ norm perturbations is larger than what you need for $ℓ^∞$ perturbations, because the volume of the $ℓ^2$ ball is proportional to $\sqrt{n}$ times the volume of the $ℓ^∞$ ball, where n is the input dimension."
I could not understand the explanation well (the volume of the $ℓ^2$ ball is proportional to $\sqrt{n}$ times the volume of the $ℓ^∞$ ball).
I wonder how the volume of $ℓ^2$ norm ball is larger than $ℓ^∞$ ball in 784-dimensional space.
I was thinking that the volume of $ℓ^∞$ norm ball should be bigger, so I am a bit confused. Can somebody help and explain please?
Here is the link for the post: https://adversarial-ml-tutorial.org/adversarial_examples/