  In this paper, we present a comprehensive survey of the current trends
focusing specifically on physical adversarial attacks. We aim to provide a
thorough understanding of the concept of physical adversarial attacks,
analyzing their key characteristics and distinguishing features. Furthermore,
we explore the specific requirements and challenges associated with executing
attacks in the physical world. Our article delves into various physical
adversarial attack methods, categorized according to their target tasks in
different applications, including classification, detection, face recognition,
semantic segmentation and depth estimation. We assess the performance of these
attack methods in terms of their effectiveness, stealthiness, and robustness.
We examine how each technique strives to ensure the successful manipulation of
DNNs while mitigating the risk of detection and withstanding real-world
distortions. Lastly, we discuss the current challenges and outline potential
future research directions in the field of physical adversarial attacks. We
highlight the need for enhanced defense mechanisms, the exploration of novel
attack strategies, the evaluation of attacks in different application domains,
and the establishment of standardized benchmarks and evaluation criteria for
physical adversarial attacks. Through this comprehensive survey, we aim to
provide a valuable resource for researchers, practitioners, and policymakers to
gain a holistic understanding of physical adversarial attacks in computer
vision and facilitate the development of robust and secure DNN-based systems.
