Title: Detecting Temporal Misalignment Attacks in Multimodal Fusion for Autonomous Driving
Primary area: applications to robotics, autonomy, planning
Keywords: Multimodal Fusion, Temporal Misalignment Attack, Attack Detection, Autonomous Driving

Abstract: Multimodal fusion (MMF) is crucial for autonomous driving perception, combining camera and LiDAR streams for reliable scene understanding. However, its reliance on precise temporal synchronization introduces a vulnerability: adversaries can exploit network-induced delays to subtly misalign sensor streams, degrading MMF performance. To address this, we propose AION, a lightweight, plug-in defense tailored for the autonomous driving scenario. AION integrates continuity-aware contrastive learning to learn smooth multimodal representations and a DTW-based detection mechanism to trace temporal alignment paths and generate misalignment scores. Experiments on both KITTI and nuScenes datasets show that AION achieves AUROC 0.92–0.98 with low false-positive rates across fusion backbones. Code will be publicly released upon acceptance at \url{https://anonymous.4open.science/r/AION-F10B}.

TL;DR: We propose AION, a plug-in defense for autonomous driving that combines Continuity-Aware Contrastive Learning and DTW-based anomaly scoring to detect subtle temporal misalignments in multimodal sensor data.