Fight light with light: A review of physical adversarial attack within light transmission pipeline

Abstract

Deep Neural Networks (DNNs) remain vulnerable to physical adversarial attacks. Attacks that target the light transmission pipeline exhibit heightened stealthiness while posing severe real-world threats due to their flexible and deployable nature. To advance the understanding of this emerging threat, we establish a unified framework that systematically analyzes the entire light transmission pipeline as a contiguous attack surface. Within this framework, we identify two primary attack vectors, manipulating light transmission channel and attacking image perception device, and systematically characterize their methodologies across nine key attributes. We further formalize the optimization process for generating adversarial light patterns and assess the physical deployment methods of such attacks. Furthermore, we propose a graded framework for evaluating the transferability and demonstrate that while physical adversarial examples in this domain exhibit high stealthiness, their transferability across different model architectures remains limited. Finally, we outline current challenges and discuss future research directions.