  The benefits of utilizing spatial context in fast object detection algorithms
have been studied extensively. Detectors increase inference speed by doing a
single forward pass per image which means they implicitly use contextual
reasoning for their predictions. However, one can show that an adversary can
design adversarial patches which do not overlap with any objects of interest in
the scene and exploit contextual reasoning to fool standard detectors. In this
paper, we examine this problem and design category specific adversarial patches
which make a widely used object detector like YOLO blind to an attacker chosen
object category. We also show that limiting the use of spatial context during
object detector training improves robustness to such adversaries. We believe
the existence of context based adversarial attacks is concerning since the
adversarial patch can affect predictions without being in vicinity of any
objects of interest. Hence, defending against such attacks becomes challenging
and we urge the research community to give attention to this vulnerability.
