CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild

Yang Zhang, Hassan Foroosh, Philip David, Boqing Gong

Abstract: In this paper, we conduct an intriguing experimental study about the physical adversarial attack on object detectors in the wild. In particular, we learn a camouflage pattern to hide vehicles from being detected by state-of-the-art convolutional neural network based detectors. Our approach alternates between two threads. In the first, we train a neural approximation function to imitate how a simulator applies a camouflage to vehicles and how a vehicle detector performs given images of the camouflaged vehicles. In the second, we minimize the approximated detection score by searching for the optimal camouflage. Experiments show that the learned camouflage can not only hide a vehicle from the image-based detectors under many test cases but also generalizes to different environments, vehicles, and object detectors.

Keywords: Adversarial Attack, Object Detection, Synthetic Simulation

TL;DR: We propose a method to learn physical vehicle camouflage to adversarially attack object detectors in the wild. We find our camouflage effective and transferable.

Data: [KITTI](https://paperswithcode.com/dataset/kitti), [MS COCO](https://paperswithcode.com/dataset/coco)