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.

翻译：目标检测是计算机视觉中的关键任务，已成为众多关键系统不可或缺的组成部分。然而，与分类模型类似，最先进的目标检测器易受微小对抗扰动的影响，这些扰动可能显著改变其正常行为。与分类任务不同，目标检测的鲁棒性尚未得到充分探索。本研究迈出了弥合分类与目标检测鲁棒性差距的第一步，通过利用对抗训练的分类模型来实现。仅将对抗训练模型作为目标检测的骨干网络并不能带来鲁棒性。我们提出了针对分类骨干网络的有效改进，在不产生任何计算开销的情况下为目标检测注入鲁棒性。为进一步增强改进骨干网络所实现的鲁棒性，我们引入了两个轻量级组件：模仿损失与延迟对抗训练。通过在MS-COCO和Pascal VOC数据集上的大量实验，证明了所提方法的有效性。