Robust Object Detection Against Adversarial Perturbations with Gabor Filter

Adversarial attacks are one of the most critical threats in the machine learning field, which raises doubts about the application of deep neural networks (DNNs). Despite the recent advances in DNNs, the adversarial robustness in DNNs has yet to reach an acceptable level, especially against different kinds of perturbations. In this paper, we aim to enhance the robustness of object detection against adversarial perturbations. To this end, we adversarially train YOLOv3 model with different backbones by means of parameterized Gabor convolutional layers. To assess the robustness of our trained models, we have adopted TOG vanishing, TOG fabrication, and TOG mislabeling adversarial attacks. These perturbations are crafted on PASCAL VOC and MSCOCO datasets to simulate three types of targeted specificity, including object-vanishing, object-fabrication, and object-mislabeling, respectively. Extensive evaluations demonstrate that our model equipped with the Gabor filters gain consideration adversarial robustness in addition to the high generalization performance on clean data.