PCLD: Point Cloud Layerwise Diffusion for Adversarial Purification
Mert Gulsen Batuhan Cengiz Yusuf H. Sahin Gozde Unal
Point clouds are extensively employed in a variety of real-world applications
such as robotics, autonomous driving and augmented reality. Despite the recent
success of point cloud neural networks, especially for safety-critical tasks,
it is essential to also ensure the robustness of the model. A typical way to
assess a model's robustness is through adversarial attacks, where test-time
examples are generated based on gradients to deceive the model. While many
different defense mechanisms are studied in 2D, studies on 3D point clouds have
been relatively limited in the academic field. Inspired from PointDP, which
denoises the network inputs by diffusion, we propose Point Cloud Layerwise
Diffusion (PCLD), a layerwise diffusion based 3D point cloud defense strategy.
Unlike PointDP, we propagated the diffusion denoising after each layer to
incrementally enhance the results. We apply our defense method to different
types of commonly used point cloud models and adversarial attacks to evaluate
its robustness. Our experiments demonstrate that the proposed defense method
achieved results that are comparable to or surpass those of existing
methodologies, establishing robustness through a novel technique. Code is
available at https://github.com/batuceng/diffusion-layer-robustness-pc.