  Though it is well known that the performance of deep neural networks (DNNs)
degrades under certain light conditions, there exists no study on the threats
of light beams emitted from some physical source as adversarial attacker on
DNNs in a real-world scenario. In this work, we show by simply using a laser
beam that DNNs are easily fooled. To this end, we propose a novel attack method
called Adversarial Laser Beam ($AdvLB$), which enables manipulation of laser
beam's physical parameters to perform adversarial attack. Experiments
demonstrate the effectiveness of our proposed approach in both digital- and
physical-settings. We further empirically analyze the evaluation results and
reveal that the proposed laser beam attack may lead to some interesting
prediction errors of the state-of-the-art DNNs. We envisage that the proposed
$AdvLB$ method enriches the current family of adversarial attacks and builds
the foundation for future robustness studies for light.
