Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2507.09095 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911489585053696 |
|---|---|
| author | Shahriar, Md Hasan Barat, Md Mohaimin Al Sundar, Harshavardhan Zhang, Ning Ramakrishnan, Naren Hou, Y. Thomas Lou, Wenjing |
| author_facet | Shahriar, Md Hasan Barat, Md Mohaimin Al Sundar, Harshavardhan Zhang, Ning Ramakrishnan, Naren Hou, Y. Thomas Lou, Wenjing |
| contents | Multimodal fusion (MMF) plays a critical role in the perception of autonomous driving, which primarily fuses camera and LiDAR streams for a comprehensive and efficient scene understanding. However, its strict reliance on precise temporal synchronization exposes it to new vulnerabilities. In this paper, we introduce DejaVu, an attack that exploits the in-vehicular network to manipulate the integrity of time and create subtle temporal misalignments, severely degrading downstream MMF-based perception tasks. Our comprehensive attack analysis across different models and datasets reveals the sensors' task-specific imbalanced sensitivities: object detection is overly dependent on LiDAR inputs, while object tracking is highly reliant on the camera inputs. Consequently, with a single-frame LiDAR delay, an attacker can reduce the car detection mAP by up to 88.5%, while with a three-frame camera delay, multiple object tracking accuracy (MOTA) for car drops by 73%. We further demonstrated two attack scenarios using an automotive Ethernet testbed for hardware-in-the-loop validation and the Autoware stack for end-to-end AD simulation, demonstrating the feasibility of the DejaVu attack and its severe impact, such as collisions and phantom braking. Our code and artifacts are publicly available at: https://github.com/shahriar0651/DejaVu. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09095 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Temporal Misalignment Attacks against Multimodal Perception in Autonomous Driving Shahriar, Md Hasan Barat, Md Mohaimin Al Sundar, Harshavardhan Zhang, Ning Ramakrishnan, Naren Hou, Y. Thomas Lou, Wenjing Machine Learning Multimodal fusion (MMF) plays a critical role in the perception of autonomous driving, which primarily fuses camera and LiDAR streams for a comprehensive and efficient scene understanding. However, its strict reliance on precise temporal synchronization exposes it to new vulnerabilities. In this paper, we introduce DejaVu, an attack that exploits the in-vehicular network to manipulate the integrity of time and create subtle temporal misalignments, severely degrading downstream MMF-based perception tasks. Our comprehensive attack analysis across different models and datasets reveals the sensors' task-specific imbalanced sensitivities: object detection is overly dependent on LiDAR inputs, while object tracking is highly reliant on the camera inputs. Consequently, with a single-frame LiDAR delay, an attacker can reduce the car detection mAP by up to 88.5%, while with a three-frame camera delay, multiple object tracking accuracy (MOTA) for car drops by 73%. We further demonstrated two attack scenarios using an automotive Ethernet testbed for hardware-in-the-loop validation and the Autoware stack for end-to-end AD simulation, demonstrating the feasibility of the DejaVu attack and its severe impact, such as collisions and phantom braking. Our code and artifacts are publicly available at: https://github.com/shahriar0651/DejaVu. |
| title | Temporal Misalignment Attacks against Multimodal Perception in Autonomous Driving |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.09095 |