Adversarially Guided Diffusion for LiDAR Range Image Synthesis

Jul 14, 2026

Researchers introduce the first diffusion-based unrestricted adversarial attack targeting 2D LiDAR range-image segmentation. Their method leverages adversarial guidance from segmentation loss during diffusion sampling to generate realistic adversarial examples that induce structured segmentation errors while remaining close to the LiDAR data manifold. Experiments on the SemanticKITTI dataset show that the attack enables adjustable degradation and transfers across different segmentation architectures, outperforming traditional norm-bounded baselines in effectiveness-realism trade-offs.

Why it matters: This work reveals a novel vulnerability in LiDAR-based perception for autonomous vehicles, raising safety concerns due to the realism and transferability of the generated adversarial examples.

Full story at: arXiv Computer Vision ↗