fra31
Fixed evaluation of models with random defenses
Thank you for your outstanding contributions.
@LYMDLUT and I put forward this PR to improve the evaluation of models with random defenses.
We've noticed that AutoAttack's current strategy for selecting the final output (clean/APGD etc) based on one time evaluation, regardless of whether the target models implement random defenses or not. This overlooks the variability of outputs in models with random defenses.
Relying on a single evaluation to filter samples for subsequent attacks leads to inflated success rate and hinders the exploration of attack methods that could potentially yield superior outcomes.
To address this, we propose to perform multiple time evaluations for models with random defenses and chose the adversarial example with the highest robustness as final output.
