Small, carefully crafted perturbations called adversarial perturbations can
easily fool neural networks. However, these perturbations are largely additive
and not naturally found. We turn our attention to the field of Autonomous
navigation wherein adverse weather conditions such as fog have a drastic effect
on the predictions of these systems. These weather conditions are capable of
acting like natural adversaries that can help in testing models. To this end,
we introduce a general notion of adversarial perturbations, which can be
created using generative models and provide a methodology inspired by
Cycle-Consistent Generative Adversarial Networks to generate adversarial
weather conditions for a given image. Our formulation and results show that
these images provide a suitable testbed for steering models used in Autonomous
navigation models. Our work also presents a more natural and general definition
of Adversarial perturbations based on Perceptual Similarity.Comment: Accepted to WACV 202