In this paper, we study physical adversarial attacks on object detectors in the wild.
Previous works on this matter mostly craft instance-dependent perturbations
only for rigid and planar objects.
To this end, we propose to learn an adversarial pattern to effectively
attack all instances belonging to the same object category (e.g., person, car),
referred to as Universal Physical Camouflage Attack (UPC).
Concretely, UPC crafts camouflage by jointly fooling the region proposal network,
as well as misleading the classifier and the regressor to output errors.
In order to make UPC effective for articulated non-rigid or non-planar objects,
we introduce a set of transformations for the generated camouflage patterns to
mimic their deformable properties.
We additionally impose optimization constraint to make generated patterns look
natural to human observers. To fairly evaluate the effectiveness of different
physical-world attacks on object detectors, we present the first standardized
virtual database, AttackScenes, which simulates the real 3D world in a controllable
and reproducible environment. Extensive experiments suggest the superiority of
our proposed UPC compared with existing physical adversarial attackers not only
in virtual environments (AttackScenes), but also in real-world physical environments.
Our paper proposed the G-UAP which is the first work to craft universal
adversarial perturbations to fool the RPN-based detectors. G-UAP focuses
on misleading the foreground prediction of RPN to background to make detectors