Efficient Model-Based Purification Against Adversarial Attacks for LiDAR Segmentation

TL;DR

This paper presents an efficient, explainable model-based purification method to defend 2D range view LiDAR segmentation networks against adversarial attacks, enhancing safety in autonomous vehicles with minimal computational cost.

Contribution

It introduces a novel lightweight purification framework specifically designed for 2D range view LiDAR segmentation, with a mathematically justified optimization approach for adversarial defense.

Findings

Achieves strong adversarial resilience with low computational overhead.

Outperforms existing generative and adversarial training baselines.

Demonstrates effective real-world deployment on an autonomous vehicle.

Abstract

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.