# Temporal Consistency Checks to Detect LiDAR Spoofing Attacks on Autonomous Vehicle Perception

arXiv: https://arxiv.org/abs/2106.07833 · HTML: https://mlsanity.com/paper/2106.07833/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving](https://mlsanity.com/paper/1907.06826/) (2019)
- [MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps](https://mlsanity.com/paper/2003.06754/) (2020)
- [Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures](https://mlsanity.com/paper/2006.16974/) (2020)
- [Object Removal Attacks on LiDAR-based 3D Object Detectors](https://mlsanity.com/paper/2102.03722/) (2021)

## Built on by (most cited first, 8 of 8)

- [SoK: On the Semantic AI Security in Autonomous Driving](https://mlsanity.com/paper/2203.05314/) (2022)
- [Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification](https://mlsanity.com/paper/2205.10906/) (2022)
- [ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency](https://mlsanity.com/paper/2310.14504/) (2023)
- [A Novel Multi-layer Task-centric and Data Quality Framework for Autonomous Driving](https://mlsanity.com/paper/2506.17346/) (2025)
- [Adversarial 3D Virtual Patches using Integrated Gradients](https://mlsanity.com/paper/2406.00282/) (2024)
- [Explainability-Aware Frustum Attack: Exposing Structural Vulnerabilities in LiDAR-Based 3D Object Detectors](https://mlsanity.com/paper/2606.29963/) (2026)
- [Adversarial Calibration Attack on Autonomous Vehicles](https://mlsanity.com/paper/2608.28778/) (2026)
- [Adversarial Robustness of Camera-Radar Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2610.16012/) (2026)

## Abstract (arXiv)

LiDAR sensors are used widely in Autonomous Vehicles for better perceiving the environment which enables safer driving decisions. Recent work has demonstrated serious LiDAR spoofing attacks with alarming consequences. In particular, model-level LiDAR spoofing attacks aim to inject fake depth measurements to elicit ghost objects that are erroneously detected by 3D Object Detectors, resulting in hazardous driving decisions. In this work, we explore the use of motion as a physical invariant of genuine objects for detecting such attacks. Based on this, we propose a general methodology, 3D Temporal Consistency Check (3D-TC2), which leverages spatio-temporal information from motion prediction to verify objects detected by 3D Object Detectors. Our preliminary design and implementation of a 3D-TC2 prototype demonstrates very promising performance, providing more than 98% attack detection rate with a recall of 91% for detecting spoofed Vehicle (Car) objects, and is able to achieve real-time detection at 41Hz