GF-Score: Certified Class-Conditional Robustness Evaluation with Fairness Guarantees
Adversarial robustness is essential for deploying neural networks in safety-critical applications, yet standard evaluation methods either require expensive adversarial attacks or report only a single aggregate score that obscures how robustness is distributed across classes. We introduce the \emph{GF-Score} (GREAT-Fairness Score), a framework that decomposes the certified GREAT Score into per-class robustness profiles and quantifies their disparity through four metrics grounded in welfare economics: the Robustness Disparity Index (RDI), the Normalized Robustness Gini Coefficient (NRGC), Worst-Case Class Robustness (WCR), and a Fairness-Penalized GREAT Score (FP-GREAT). The framework furth...
| Authors | Arya Shah, Kaveri Visavadiya, Manisha Padala |
|---|---|
| Primary category | cs.LG |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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