1 research outputs found
It's Raining Cats or Dogs? Adversarial Rain Attack on DNN Perception
Rain is a common phenomenon in nature and an essential factor for many deep
neural network (DNN) based perception systems. Rain can often post inevitable
threats that must be carefully addressed especially in the context of safety
and security-sensitive scenarios (e.g., autonomous driving). Therefore, a
comprehensive investigation of the potential risks of the rain to a DNN is of
great importance. Unfortunately, in practice, it is often rather difficult to
collect or synthesize rainy images that can represent all raining situations
that possibly occur in the real world. To this end, in this paper, we start
from a new perspective and propose to combine two totally different studies,
i.e., rainy image synthesis and adversarial attack. We present an adversarial
rain attack, with which we could simulate various rainy situations with the
guidance of deployed DNNs and reveal the potential threat factors that can be
brought by rain, helping to develop more rain-robust DNNs. In particular, we
propose a factor-aware rain generation that simulates rain steaks according to
the camera exposure process and models the learnable rain factors for
adversarial attack. With this generator, we further propose the adversarial
rain attack against the image classification and object detection, where the
rain factors are guided by the various DNNs. As a result, it enables to
comprehensively study the impacts of the rain factors to DNNs. Our largescale
evaluation on three datasets, i.e., NeurIPS'17 DEV, MS COCO and KITTI,
demonstrates that our synthesized rainy images can not only present visually
realistic appearances, but also exhibit strong adversarial capability, which
builds the foundation for further rain-robust perception studies