  Recent research has demonstrated that deep neural networks (DNNs) are
vulnerable to adversarial perturbations. Therefore, it is imperative to
evaluate the resilience of advanced DNNs to adversarial attacks. However,
traditional methods that use stickers as physical perturbations to deceive
classifiers face challenges in achieving stealthiness and are susceptible to
printing loss. Recently, advancements in physical attacks have utilized light
beams, such as lasers, to perform attacks, where the optical patterns generated
are artificial rather than natural. In this work, we propose a black-box
projector-based physical attack, referred to as adversarial color projection
(AdvCP), which manipulates the physical parameters of color projection to
perform an adversarial attack. We evaluate our approach on three crucial
criteria: effectiveness, stealthiness, and robustness. In the digital
environment, we achieve an attack success rate of 97.60% on a subset of
ImageNet, while in the physical environment, we attain an attack success rate
of 100% in the indoor test and 82.14% in the outdoor test. The adversarial
samples generated by AdvCP are compared with baseline samples to demonstrate
the stealthiness of our approach. When attacking advanced DNNs, experimental
results show that our method can achieve more than 85% attack success rate in
all cases, which verifies the robustness of AdvCP. Finally, we consider the
potential threats posed by AdvCP to future vision-based systems and
applications and suggest some ideas for light-based physical attacks.
