Adversarial Patches Exploiting Contextual Reasoning in Object Detection

The usefulness of spatial context in most fast object detection algorithms that do a single forward pass per image is well known where they utilize context to improve their accuracy. In fact, they must do it to increase the inference speed by processing the image just once. We show that an adversary can attack the model by exploiting contextual reasoning. We develop adversarial attack algorithms that make an object detector blind to a particular category chosen by the adversary even though the patch does not overlap with the missed detections. We also show that limiting the use of contextual reasoning in learning the object detector acts as a form of defense that improves the accuracy of the detector after an attack. We believe defending against our practical adversarial attack algorithms is not easy and needs attention from the research community.