  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 nonplanar 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. The code
of FCA will be available at:
https://idrl-lab.github.io/Full-coverage-camouflage-adversarial-attack/.
