Accuracy-Robustness Area (ARA)

2000년 도입 · 논문 3편에서 사용

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 $\epsilon$.

출처: Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness

소개 논문: Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness