Enhancing real-world adversarial patches through 3D modeling of complex target scenes

Yael Mathov, Lior Rokach, Yuval Elovici

Neurocomputing 499, 11-22, 2022

Adversarial examples have proven to be a concerning threat to deep learning models, particularly in the image domain. While many studies have examined adversarial examples in the real world, most of them relied on 2D photos of the attack scene. As a result, the attacks proposed may have limited effectiveness when implemented in realistic environments with 3D objects or varied conditions. Some studies on adversarial learning have used 3D objects, however in many cases, other researchers are unable to replicate the real-world evaluation process. In this study, we present a framework that uses 3D modeling to craft adversarial patches for an existing real-world scene. Our approach uses a 3D digital approximation of the scene to simulate the real world. With the ability to add and manipulate any element in the digital scene, our framework enables the attacker to improve the adversarial patch’s impact in real …