FROD: Robust Object Detection for Free
Muhammad Awais Weiming Zhuang Lingjuan Lyu Sung-Ho Bae
Object detection is a vital task in computer vision and has become an
integral component of numerous critical systems. However, state-of-the-art
object detectors, similar to their classification counterparts, are susceptible
to small adversarial perturbations that can significantly alter their normal
behavior. Unlike classification, the robustness of object detectors has not
been thoroughly explored. In this work, we take the initial step towards
bridging the gap between the robustness of classification and object detection
by leveraging adversarially trained classification models. Merely utilizing
adversarially trained models as backbones for object detection does not result
in robustness. We propose effective modifications to the classification-based
backbone to instill robustness in object detection without incurring any
computational overhead. To further enhance the robustness achieved by the
proposed modified backbone, we introduce two lightweight components: imitation
loss and delayed adversarial training. Extensive experiments on the MS-COCO and
Pascal VOC datasets are conducted to demonstrate the effectiveness of our
proposed approach.