  Deep neural networks (DNNs) have demonstrated high vulnerability to
adversarial examples, raising broad security concerns about their applications.
Besides the attacks in the digital world, the practical implications of
adversarial examples in the physical world present significant challenges and
safety concerns. However, current research on physical adversarial examples
(PAEs) lacks a comprehensive understanding of their unique characteristics,
leading to limited significance and understanding. In this paper, we address
this gap by thoroughly examining the characteristics of PAEs within a practical
workflow encompassing training, manufacturing, and re-sampling processes. By
analyzing the links between physical adversarial attacks, we identify
manufacturing and re-sampling as the primary sources of distinct attributes and
particularities in PAEs. Leveraging this knowledge, we develop a comprehensive
analysis and classification framework for PAEs based on their specific
characteristics, covering over 100 studies on physical-world adversarial
examples. Furthermore, we investigate defense strategies against PAEs and
identify open challenges and opportunities for future research. We aim to
provide a fresh, thorough, and systematic understanding of PAEs, thereby
promoting the development of robust adversarial learning and its application in
open-world scenarios to provide the community with a continuously updated list
of physical world adversarial sample resources, including papers, code, \etc,
within the proposed framework
