[2605.00880] Adversarial Flow Matching for Imperceptible Attacks on End-to-End Autonomous Driving
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.00880 (cs)
[Submitted on 26 Apr 2026]
Title:Adversarial Flow Matching for Imperceptible Attacks on End-to-End Autonomous Driving
Authors:Xinyu Zeng, Xiangkun He, Lei Tao, Chen Lv, Hong Cheng
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Abstract:Autonomous driving (AD) is evolving towards end-to-end (E2E) frameworks through two primary paradigms: monolithic models exemplified by Vision-Language-Action (VLA), and specialized modular architectures. Despite their divergent designs, both paradigms increasingly rely on Transformer backbones for complex reasoning, potentially causing a shared vulnerability: visually imperceptible perturbations can manipulate E2E AD models into hazardous maneuvers by targeting the Transformer module. Most existing adversarial attack approaches against AD systems operate under white-box or black-box settings; yet, they typically necessitate full model transparency, or suffer from either prohibitive query latency or limited attack transferability. In this paper, we propose Adversarial Flow Matching (AFM), a novel gray-box attack framework that exploits Transformer structural vulnerabilities in E2E AD models. AFM enables efficient one-step generation of adversarial examples via a neural average velocity field. Additionally, the proposed technique yields effective and visually imperceptible attacks by synergistically perturbing the generative latent space and the neural average velocity field. Extensive experiments demonstrate that AFM achieves a superior trade-off between attack effectiveness and imperceptibility: it substantially degrades the performance of both VLA and modular AD agents across various scenarios compared to baselines, while maintaining state-of-the-art visual imperceptibility. Furthermore, adversarial examples generated by AFM exhibit robust cross-model transferability, indicating that AFM closely approximates a black-box attack setting while requiring only the prior knowledge that the target AD model incorporates a Transformer-based module.
Comments: 16 pages, 11 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.00880 [cs.CV]
(or arXiv:2605.00880v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.00880
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arXiv-issued DOI via DataCite
Submission history
From: Lei Tao [view email] [v1] Sun, 26 Apr 2026 09:12:32 UTC (26,385 KB)
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