Efficient Model-Based Purification Against Adversarial Attacks for LiDAR Segmentation
This addresses safety-critical adversarial vulnerabilities in autonomous driving perception, offering a lightweight defense for widely used 2D range-view pipelines, though it is incremental as it builds on existing purification concepts.
The paper tackles the problem of adversarial attacks on LiDAR segmentation for autonomous vehicles by introducing an efficient model-based purification framework for 2D range-view representations, achieving competitive performance on benchmarks and demonstrating accurate operation in real-world deployment.
LiDAR-based segmentation is essential for reliable perception in autonomous vehicles, yet modern segmentation networks are highly susceptible to adversarial attacks that can compromise safety. Most existing defenses are designed for networks operating directly on raw 3D point clouds and rely on large, computationally intensive generative models. However, many state-of-the-art LiDAR segmentation pipelines operate on more efficient 2D range view representations. Despite their widespread adoption, dedicated lightweight adversarial defenses for this domain remain largely unexplored. We introduce an efficient model-based purification framework tailored for adversarial defense in 2D range-view LiDAR segmentation. We propose a direct attack formulation in the range-view domain and develop an explainable purification network based on a mathematical justified optimization problem, achieving strong adversarial resilience with minimal computational overhead. Our method achieves competitive performance on open benchmarks, consistently outperforming generative and adversarial training baselines. More importantly, real-world deployment on a demo vehicle demonstrates the framework's ability to deliver accurate operation in practical autonomous driving scenarios.