{"ID":2895337,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09095","arxiv_id":"2507.09095","title":"Temporal Misalignment Attacks against Multimodal Perception in Autonomous Driving","abstract":"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.","short_abstract":"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 Dej...","url_abs":"https://arxiv.org/abs/2507.09095","url_pdf":"https://arxiv.org/pdf/2507.09095v3","authors":"[\"Md Hasan Shahriar\",\"Md Mohaimin Al Barat\",\"Harshavardhan Sundar\",\"Ning Zhang\",\"Naren Ramakrishnan\",\"Y. Thomas Hou\",\"Wenjing Lou\"]","published":"2025-07-12T00:44:26Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":612178,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2895337,"paper_url":"https://arxiv.org/abs/2507.09095","paper_title":"Temporal Misalignment Attacks against Multimodal Perception in Autonomous Driving","repo_url":"https://github.com/shahriar0651/DejaVu","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}