  Vision-based perception modules are increasingly deployed in many
applications, especially autonomous vehicles and intelligent robots. These
modules are being used to acquire information about the surroundings and
identify obstacles. Hence, accurate detection and classification are essential
to reach appropriate decisions and take appropriate and safe actions at all
times. Current studies have demonstrated that "printed adversarial attacks",
known as physical adversarial attacks, can successfully mislead perception
models such as object detectors and image classifiers. However, most of these
physical attacks are based on noticeable and eye-catching patterns for
generated perturbations making them identifiable/detectable by human eye or in
test drives. In this paper, we propose a camera-based inconspicuous adversarial
attack (\textbf{AdvRain}) capable of fooling camera-based perception systems
over all objects of the same class. Unlike mask based fake-weather attacks that
require access to the underlying computing hardware or image memory, our attack
is based on emulating the effects of a natural weather condition (i.e.,
Raindrops) that can be printed on a translucent sticker, which is externally
placed over the lens of a camera. To accomplish this, we provide an iterative
process based on performing a random search aiming to identify critical
positions to make sure that the performed transformation is adversarial for a
target classifier. Our transformation is based on blurring predefined parts of
the captured image corresponding to the areas covered by the raindrop. We
achieve a drop in average model accuracy of more than $45\%$ and $40\%$ on
VGG19 for ImageNet and Resnet34 for Caltech-101, respectively, using only $20$
raindrops.
