FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack

• URL: http://arxiv.org/abs/2109.07193v1
• Date: Wed, 15 Sep 2021 10:17:12 GMT
• Title: FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
• Authors: DonghuaWang, Tingsong Jiang, Jialiang Sun, Weien Zhou, Xiaoya Zhang, Zhiqiang Gong, Wen Yao and Xiaoqian Chen
• Abstract summary: We propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors. Specifically, we first try rendering the non-planar camouflage texture over the full vehicle surface. We then introduce a transformation function to transfer the rendered camouflaged vehicle into a photo-realistic scenario.
• Score: 5.476797414272598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an individual adversarial patch, which only covers the planar part of the vehicle's surface and fails to attack the detector in physical scenarios for multi-view, long-distance and partially occluded objects. To bridge the gap between digital attacks and physical attacks, we exploit the full 3D vehicle surface to propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors. Specifically, we first try rendering the non-planar camouflage texture over the full vehicle surface. To mimic the real-world environment conditions, we then introduce a transformation function to transfer the rendered camouflaged vehicle into a photo-realistic scenario. Finally, we design an efficient loss function to optimize the camouflage texture. Experiments show that the full-coverage camouflage attack can not only outperform state-of-the-art methods under various test cases but also generalize to different environments, vehicles, and object detectors.

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