  The AutoAttack (AA) has been the most reliable method to evaluate adversarial
robustness when considerable computational resources are available. However,
the high computational cost (e.g., 100 times more than that of the project
gradient descent attack) makes AA infeasible for practitioners with limited
computational resources, and also hinders applications of AA in the adversarial
training (AT). In this paper, we propose a novel method, minimum-margin (MM)
attack, to fast and reliably evaluate adversarial robustness. Compared with AA,
our method achieves comparable performance but only costs 3% of the
computational time in extensive experiments. The reliability of our method lies
in that we evaluate the quality of adversarial examples using the margin
between two targets that can precisely identify the most adversarial example.
The computational efficiency of our method lies in an effective Sequential
TArget Ranking Selection (STARS) method, ensuring that the cost of the MM
attack is independent of the number of classes. The MM attack opens a new way
for evaluating adversarial robustness and provides a feasible and reliable way
to generate high-quality adversarial examples in AT.

Machine Learning;Cryptography and Security