Systems and methods for adversarially robust object detection

Abstract

Described herein are embodiments for an approach to improve the robustness of an object detector against adversarial attacks. Existing attacks for object detectors and the impacts of individual task component on model robustness are systematically analyzed from a multi-task view of object detection. In one or more embodiments, a multi-task learning perspective of object detection is introduced and an asymmetric role of task losses is identified. One or more embodiments of an adversarial training method for robust object detection are presented to leverage the multiple sources of attacks for improving the robustness of object detection models.