What is: Fast Minimum-Norm Attack?

Fast Minimum-Norm Attack, or FNM, is a type of adversarial attack that works with different $\ell_{p}$-norm perturbation models ($p=0,1,2,\infty$), is robust to hyperparameter choices, does not require adversarial starting points, and converges within few lightweight steps. It works by iteratively finding the sample misclassified with maximum confidence within an $\ell_{p}$-norm constraint of size $\epsilon$, while adapting $\epsilon$ to minimize the distance of the current sample to the decision boundary.