  Recently, Deep Neural Networks (DNNs) have achieved remarkable performances
in many applications, while several studies have enhanced their vulnerabilities
to malicious attacks. In this paper, we emulate the effects of natural weather
conditions to introduce plausible perturbations that mislead the DNNs. By
observing the effects of such atmospheric perturbations on the camera lenses,
we model the patterns to create different masks that fake the effects of rain,
snow, and hail. Even though the perturbations introduced by our attacks are
visible, their presence remains unnoticed due to their association with natural
events, which can be especially catastrophic for fully-autonomous and unmanned
vehicles. We test our proposed fakeWeather attacks on multiple Convolutional
Neural Network and Capsule Network models, and report noticeable accuracy drops
in the presence of such adversarial perturbations. Our work introduces a new
security threat for DNNs, which is especially severe for safety-critical
applications and autonomous systems.
