Adversarial Examples in the Physical World: A Survey
Jiakai Wang Donghua Wang Jin Hu Siyang Wu Tingsong Jiang Wen Yao Aishan Liu Xianglong Liu
Deep neural networks (DNNs) have demonstrated high vulnerability to
adversarial examples. 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.