  Adversarial attacks against deep learning-based object detectors have been
studied extensively in the past few years. Most of the attacks proposed have
targeted the model's integrity (i.e., caused the model to make incorrect
predictions), while adversarial attacks targeting the model's availability, a
critical aspect in safety-critical domains such as autonomous driving, have not
yet been explored by the machine learning research community. In this paper, we
propose a novel attack that negatively affects the decision latency of an
end-to-end object detection pipeline. We craft a universal adversarial
perturbation (UAP) that targets a widely used technique integrated in many
object detector pipelines -- non-maximum suppression (NMS). Our experiments
demonstrate the proposed UAP's ability to increase the processing time of
individual frames by adding "phantom" objects that overload the NMS algorithm
while preserving the detection of the original objects which allows the attack
to go undetected for a longer period of time.
