  Recently, physical domain adversarial attacks have drawn significant
attention from the machine learning community. One important attack proposed by
Eykholt et al. can fool a classifier by placing black and white stickers on an
object such as a road sign. While this attack may pose a significant threat to
visual classifiers, there are currently no defenses designed to protect against
this attack. In this paper, we propose new defenses that can protect against
multi-sticker attacks. We present defensive strategies capable of operating
when the defender has full, partial, and no prior information about the attack.
By conducting extensive experiments, we show that our proposed defenses can
outperform existing defenses against physical attacks when presented with a
multi-sticker attack.
