On the Importance of Backbone to the Adversarial Robustness of Object
  Detectors
Xiao Li Hang Chen Xiaolin Hu
Object detection is a critical component of various security-sensitive
applications, such as autonomous driving and video surveillance. However,
existing deep learning-based object detectors are vulnerable to adversarial
attacks, which poses a significant challenge to their reliability and safety.
Through experiments, we found that existing works on improving the adversarial
robustness of object detectors have given a false sense of security. We argue
that using adversarially pre-trained backbone networks is essential for
enhancing the adversarial robustness of object detectors. We propose a simple
yet effective recipe for fast adversarial fine-tuning on object detectors with
adversarially pre-trained backbones. Without any modifications to the structure
of object detectors, our recipe achieved significantly better adversarial
robustness than previous works. Moreover, we explore the potential of different
modern object detectors to improve adversarial robustness using our recipe and
demonstrate several interesting findings. Our empirical results set a new
milestone and deepen the understanding of adversarially robust object
detection. Code and trained checkpoints will be publicly available.