Methods › General › Adversarial Training › Accuracy-Robustness Area (ARA)
Accuracy-Robustness Area
Accuracy-Robustness Area (ARA)
Introduced by Walt Woods et al. in Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
In the space of adversarial perturbation against classifier accuracy, the ARA is the area between a classifier's curve and the straight line defined by a naive classifier's maximum accuracy. Intuitively, the ARA measures a combination of the classifier’s predictive power and its ability to overcome an adversary. Importantly, when contrasted against existing robustness metrics, the ARA takes into account the classifier’s performance against all adversarial examples, without bounding them by some arbitrary ϵ.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Adversarial multi-task underwater acoustic target recognition: towards robustness against various influential factors 5 Nov 2024 · 1 repository · arXiv:2411.02848
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Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness 7 Jun 2019 · 1 repository · arXiv:1906.02896
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Fake News Detection via NLP is Vulnerable to Adversarial Attacks 5 Jan 2019 · 1 repository · arXiv:1901.09657
Tasks archive 2025-07-28
9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections