Fixed evaluation of models with random defenses #105
+50
−18
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
@fra31 Could you please review this pr？ |
|
您好，您的邮件已收到!
|
|
In Appendix L of our paper, we provide a detailed report on our fix for AutoAttack and its impact. We encourage future research to adopt this updated version when evaluating models with randomness, as it effectively reduces the risk of overestimating robustness. If you find our work useful for your research, please consider citing it: @Article{liu2024towards,
|
|
您好，您的邮件已收到!
|
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
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.