  In deep learning applications, robustness measures the ability of neural
models that handle slight changes in input data, which could lead to potential
safety hazards, especially in safety-critical applications. Pre-deployment
assessment of model robustness is essential, but existing methods often suffer
from either high costs or imprecise results. To enhance safety in real-world
scenarios, metrics that effectively capture the model's robustness are needed.
To address this issue, we compare the rigour and usage conditions of various
assessment methods based on different definitions. Then, we propose a
straightforward and practical metric utilizing hypothesis testing for
probabilistic robustness and have integrated it into the TorchAttacks library.
Through a comparative analysis of diverse robustness assessment methods, our
approach contributes to a deeper understanding of model robustness in
safety-critical applications.
