  Deep neural networks (DNNs) have demonstrated exceptional success across
various tasks, underscoring the need to evaluate the robustness of advanced
DNNs. However, traditional methods using stickers as physical perturbations to
deceive classifiers present challenges in achieving stealthiness and suffer
from printing loss. Recent advancements in physical attacks have utilized light
beams such as lasers and projectors to perform attacks, where the optical
patterns generated are artificial rather than natural. In this study, we
introduce a novel physical attack, adversarial catoptric light (AdvCL), where
adversarial perturbations are generated using a common natural phenomenon,
catoptric light, to achieve stealthy and naturalistic adversarial attacks
against advanced DNNs in a black-box setting. We evaluate the proposed method
in three aspects: effectiveness, stealthiness, and robustness. Quantitative
results obtained in simulated environments demonstrate the effectiveness of the
proposed method, and in physical scenarios, we achieve an attack success rate
of 83.5%, surpassing the baseline. We use common catoptric light as a
perturbation to enhance the stealthiness of the method and make physical
samples appear more natural. Robustness is validated by successfully attacking
advanced and robust DNNs with a success rate over 80% in all cases.
Additionally, we discuss defense strategy against AdvCL and put forward some
light-based physical attacks.
