LiDAR-SHIELD 发布：Docker 化的 LiDAR 3D 感知模型对抗鲁棒性评估框架
LiDAR-SHIELD: A Fully Dockerized Adversarial Robustness Assessment Framework for LiDAR-Based 3D Perception Models
搞自动驾驶感知的朋友可以看看，这套 Docker 化框架接模型只要写一个 loader 函数，PGD、LiDAttack 这些对抗攻击开箱即用，代码开源了。
LiDAR-SHIELD 是一个完全 Docker 化的对抗鲁棒性评估框架，面向自动驾驶和机器人中的 LiDAR 3D 感知模型。它不把目标模型装进工具包，而是通过 /info、/forward、/gradient 三个 HTTP 端点暴露任意模型，接入新模型或数据集只需一个 loader 函数。框架在同一引擎-服务协议下同时支持 3D 语义分割和 3D 目标检测，集成了针对分割的 PGD 和 Square Attack，以及针对检测的点扰动、点删除、点注入和 LiDAttack 等攻击方法。代码已在 GitHub 上开源。
LiDAR-SHIELD: A Fully Dockerized Adversarial Robustness Assessment Framework for LiDAR-Based 3D Perception Models
LiDAR-based 3D perception underpins autonomous driving and robotics, yet deploying these models safely requires stress-testing them against adversarial inputs before real-world use. Although state-of-the-art adversarial attacks exist, their implementations are often fragmented across individual repositories, each tied to a specific architecture or task and requiring manual environment reconstruction and model-specific integration code. We present LIDARSHIELD, a unified and fully Dockerized framework that reverses this workflow: rather than installing the target model inside the toolkit, LIDARSHIELD exposes any model through three lightweight HTTP endpoints, namely /info, /forward, and /gradient As a result, connecting a new model or dataset requires only a single loader function or adapter. The framework supports both 3D semantic segmentation and 3D object detection through the same engine-service protocol, integrating a task-aware suite of state-of-the-art attacks, including PGD and Square Attack for segmentation, and point perturbation, point removal, point injection, and LiDAttack for detection. This enables systematic exposure of corner cases and vulnerabilities across heterogeneous LiDAR backbones. The LiDAR-Shield codebase is publicly available at https://github.com/alexandrosgk/LiDAR-shield.