RPU-PVB: robust object detection based on a unified metric perspective with bilinear interpolation

With the development of cloud computing and deep learning, an increasing number of artificial intelligence models have been applied to reality. Such as videos on cell phones can be uploaded to the cloud for storage, which is detected by cloud arithmetic. Nevertheless, achieving this goal requires frequent consideration of the security of the model, since videos or images that go to the cloud, it is very likely to receive an adversarial attack. Regarding object detection, there has however been slow advancement in robustness research in this area. This is because training a target detection model requires a lot of arithmetic and time. Moreover, the current research has only slightly reduced the gap between clean and adversarial samples. To alleviate this problem, we propose a uniform perspective object detection robustness model based on bilinear interpolation that can accurately identify clean and adversarial samples. We propose the robustness optimization based on uniform metric perspective (RPU) for feature learning of clean and adversarial samples, drawing on the fine-grained idea. Following this, we analyze the fragility of the adversarial samples and consequently use the proposed perturbation filtering verification (PVB) based on bilinear interpolation. With slightly degraded clean sample detection performance, it substantially improves the robustness of object detection. Extensive experiments on PASCAL VOC and MS COCO show that our model guarantees the detection performance of clean samples and increases the detection performance of adversarial samples. The work we did has been open-sourced on GitHub: https://github.com/KujouRiu/RPU-PVB.