  Adversarial camouflage has garnered attention for its ability to attack
object detectors from any viewpoint by covering the entire object's surface.
However, universality and robustness in existing methods often fall short as
the transferability aspect is often overlooked, thus restricting their
application only to a specific target with limited performance. To address
these challenges, we present Adversarial Camouflage for Transferable and
Intensive Vehicle Evasion (ACTIVE), a state-of-the-art physical camouflage
attack framework designed to generate universal and robust adversarial
camouflage capable of concealing any 3D vehicle from detectors. Our framework
incorporates innovative techniques to enhance universality and robustness,
including a refined texture rendering that enables common texture application
to different vehicles without being constrained to a specific texture map, a
novel stealth loss that renders the vehicle undetectable, and a smooth and
camouflage loss to enhance the naturalness of the adversarial camouflage. Our
extensive experiments on 15 different models show that ACTIVE consistently
outperforms existing works on various public detectors, including the latest
YOLOv7. Notably, our universality evaluations reveal promising transferability
to other vehicle classes, tasks (segmentation models), and the real world, not
just other vehicles.
