  Adversarial attacks involve adding, small, often imperceptible, perturbations
to inputs with the goal of getting a machine learning model to misclassifying
them. While many different adversarial attack strategies have been proposed on
image classification models, object detection pipelines have been much harder
to break. In this paper, we propose a novel strategy to craft adversarial
examples by solving a constrained optimization problem using an adversarial
generator network. Our approach is fast and scalable, requiring only a forward
pass through our trained generator network to craft an adversarial sample.
Unlike in many attack strategies, we show that the same trained generator is
capable of attacking new images without explicitly optimizing on them. We
evaluate our attack on a trained Faster R-CNN face detector on the cropped
300-W face dataset where we manage to reduce the number of detected faces to
$0.5\%$ of all originally detected faces. In a different experiment, also on
300-W, we demonstrate the robustness of our attack to a JPEG compression based
defense typical JPEG compression level of $75\%$ reduces the effectiveness of
our attack from only $0.5\%$ of detected faces to a modest $5.0\%$.
