3DGAA: Realistic and Robust 3D Gaussian-Based Adversarial Attack for Autonomous Driving

Summary

Researchers present 3DGAA, a framework for physical adversarial attacks on camera-based perception in autonomous vehicles. It uses 3D multi-view optimization with a 3D Gaussian splatting surrogate to produce print-only vehicle wraps that keep geometry unchanged. In CARLA simulations and miniature-vehicle tests, the wraps substantially reduced detection confidence and average precision across multiple modern detectors.

Topics

• Adversarial machine learning

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