3D Adversarial

3D adversarial attacks explore how to subtly alter 3D data (point clouds, meshes, or rendered scenes) to fool deep learning models used in applications like autonomous driving and augmented reality. Current research focuses on improving the transferability of these attacks across different models and datasets, often employing techniques like diffusion models, neural radiance fields (NeRFs), and frequency-domain manipulations to generate more realistic and effective adversarial examples. This field is crucial for evaluating the robustness of 3D deep learning systems and developing effective defenses against potentially harmful manipulations of 3D data in real-world scenarios.