  Object detection has achieved promising performance on clean datasets, but
how to achieve better tradeoff between the adversarial robustness and clean
precision is still under-explored. Adversarial training is the mainstream
method to improve robustness, but most of the works will sacrifice clean
precision to gain robustness than standard training. In this paper, we propose
Unified Decoupled Feature Alignment (UDFA), a novel fine-tuning paradigm which
achieves better performance than existing methods, by fully exploring the
combination between self-knowledge distillation and adversarial training for
object detection. We first use decoupled fore/back-ground features to construct
self-knowledge distillation branch between clean feature representation from
pretrained detector (served as teacher) and adversarial feature representation
from student detector. Then we explore the self-knowledge distillation from a
new angle by decoupling original branch into a self-supervised learning branch
and a new self-knowledge distillation branch. With extensive experiments on the
PASCAL-VOC and MS-COCO benchmarks, the evaluation results show that UDFA can
surpass the standard training and state-of-the-art adversarial training methods
for object detection. For example, compared with teacher detector, our approach
on GFLV2 with ResNet-50 improves clean precision by 2.2 AP on PASCAL-VOC;
compared with SOTA adversarial training methods, our approach improves clean
precision by 1.6 AP, while improving adversarial robustness by 0.5 AP. Our code
will be available at https://github.com/grispeut/udfa.
