  We present a systematic study of adversarial attacks on state-of-the-art
object detection frameworks. Using standard detection datasets, we train
patterns that suppress the objectness scores produced by a range of commonly
used detectors, and ensembles of detectors. Through extensive experiments, we
benchmark the effectiveness of adversarially trained patches under both
white-box and black-box settings, and quantify transferability of attacks
between datasets, object classes, and detector models. Finally, we present a
detailed study of physical world attacks using printed posters and wearable
clothes, and rigorously quantify the performance of such attacks with different
metrics.
