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.

近年来，对神经网络的稳健性评估引起了极大关注，深度学习的稳健性问题特别突出，研究人员致力于在图像识别任务中评估稳健性，在此综述中，我们对神经网络评估中敌对稳健性和扰动稳健性进行了详细研究，分析当前研究和标准，提供了图像识别中稳健性评估的广泛概述，分析了概念、度量标准和评估方法，研究了用于度量图像扰动程度的扰动度量和范围表示，以及特定于分类模型稳健性条件的稳健度量，还讨论了现有方法的优势和局限性，并提供了一些未来研究的潜在方向。