  Although many studies have examined adversarial examples in the real world,
most of them relied on 2D photos of the attack scene; thus, the attacks
proposed cannot address realistic environments with 3D objects or varied
conditions. Studies that use 3D objects are limited, and in many cases, the
real-world evaluation process is not replicable by other researchers,
preventing others from reproducing the results. In this study, we present a
framework that crafts an adversarial patch for an existing real-world scene.
Our approach uses a 3D digital approximation of the scene as a simulation of
the real world. With the ability to add and manipulate any element in the
digital scene, our framework enables the attacker to improve the patch's
robustness in real-world settings. We use the framework to create a patch for
an everyday scene and evaluate its performance using a novel evaluation process
that ensures that our results are reproducible in both the digital space and
the real world. Our evaluation results show that the framework can generate
adversarial patches that are robust to different settings in the real world.
