  In recent years, there has been significant attention given to the robustness
assessment of neural networks. Robustness plays a critical role in ensuring
reliable operation of artificial intelligence (AI) systems in complex and
uncertain environments. Deep learning's robustness problem is particularly
significant, highlighted by the discovery of adversarial attacks on image
classification models. Researchers have dedicated efforts to evaluate
robustness in diverse perturbation conditions for image recognition tasks.
Robustness assessment encompasses two main techniques: robustness verification/
certification for deliberate adversarial attacks and robustness testing for
random data corruptions. In this survey, we present a detailed examination of
both adversarial robustness (AR) and corruption robustness (CR) in neural
network assessment. Analyzing current research papers and standards, we provide
an extensive overview of robustness assessment in image recognition. Three
essential aspects are analyzed: concepts, metrics, and assessment methods. We
investigate the perturbation metrics and range representations used to measure
the degree of perturbations on images, as well as the robustness metrics
specifically for the robustness conditions of classification models. The
strengths and limitations of the existing methods are also discussed, and some
potential directions for future research are provided.
