In order to deploy deep neural networks (DNNs) in high-stakes scenarios, it is imperative that DNNs provide inference robust to external perturbations - both intentional and unintentional. Although the resilience of DNNs to intentional and unintentional perturbations has been widely investigated, a unified vision of these inherently intertwined problem domains is still missing. In this work, we fill this gap by providing a survey of the state of the art and highlighting the similarities of the proposed approaches.We also analyze the research challenges that need to be addressed to deploy resilient and secure DNNs. As there has not been any such survey connecting the resilience of DNNs to intentional and unintentional perturbations, we believe this work can help advance the frontier in both domains by enabling the exchange of ideas between the two communities.

翻译：为了在关键场景中部署深度神经网络（DNNs），DNN必须提供能够抵御外部扰动（包括有意和无意扰动）的鲁棒推理。尽管DNN对有意和无意扰动的鲁棒性已得到广泛研究，但这两个本质上相互交织的问题领域仍缺乏统一的视角。本文通过综述现有技术进展并强调所提出方法的共性，填补了这一空白。我们还分析了部署鲁棒且安全的DNN所需应对的研究挑战。由于目前尚未有综述工作将DNN对有意与无意扰动的鲁棒性联系起来，我们相信这项工作能够促进两个领域间的思想交流，从而推动相关前沿研究的发展。