  Object detection is an important vision task and has emerged as an
indispensable component in many vision system, rendering its robustness as an
increasingly important performance factor for practical applications. While
object detection models have been demonstrated to be vulnerable against
adversarial attacks by many recent works, very few efforts have been devoted to
improving their robustness. In this work, we take an initial attempt towards
this direction. We first revisit and systematically analyze object detectors
and many recently developed attacks from the perspective of model robustness.
We then present a multi-task learning perspective of object detection and
identify an asymmetric role of task losses. We further develop an adversarial
training approach which can leverage the multiple sources of attacks for
improving the robustness of detection models. Extensive experiments on
PASCAL-VOC and MS-COCO verified the effectiveness of the proposed approach.
