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Adversarial Calibration Attack on Autonomous Vehicles — arXiv:2608.28778

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.28778
License (as archived): CC0-1.0
Archived: 2026-10-17T08:39:38.274Z
SHA-256 of the archived text: c5cd7d1541d6cffd52b898dc0596e414d526b8da3264497f9a08bde75efc924f
Stable URL of this page: https://metraipolis.com/documento/dc_342beb6065e6

How to cite

Adversarial Calibration Attack on Autonomous Vehicles — arXiv:2608.28778 — arXiv (en), https://arxiv.org/abs/2608.28778 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_342beb6065e6 (Metraipolis archive dc_342beb6065e6, sha256 c5cd7d1541d6cffd52b898dc0596e414d526b8da3264497f9a08bde75efc924f)

BibTeX

@misc{dc342beb6065e6,
  title = {Adversarial Calibration Attack on Autonomous Vehicles — arXiv:2608.28778},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.28778},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_342beb6065e6; sha256 c5cd7d1541d6cffd52b898dc0596e414d526b8da3264497f9a08bde75efc924f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.28778. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Autonomous vehicles (AVs) rely on accurate camera-LiDAR calibration for multimodal sensor fusion. In practice, calibration can drift due to vibration, temperature variation, or minor sensor displacement, motivating online calibration algorithms that detect and correct misalignment at runtime while allowing the vehicle to continue operating without a factory visit. Existing AV attacks largely assume correct calibration. We instead identify online sensor calibration as a new attack plane. A corrupted calibration update can persist across subsequent fusion operations, causing system-wide errors that propagate from perception to planning and control. We present Adversarial Calibration Attack (ACA), the first physical attack against camera-LiDAR online calibration. Using a single adversarial poster, ACA first spoofs the miscalibration detector to trigger the calibration process and then steers the calibration estimator toward an incorrect transformation. A unified optimization jointly designs the poster's geometry and texture for both objectives. We evaluate ACA across benchmark datasets, simulation, and physical experiments. On benchmark datasets such as KITTI and nuScenes, ACA induces up to 33.9 degrees mean rotational calibration error, thereby severely degrading object detection. In the CARLA simulator, the attack causes a collision when the corrupted calibration is accepted in vulnerable scenarios crafted by the attacker. On a real Husky robot, a printed adversarial poster successfully reproduces the calibration error. These results demonstrate that online calibration is a practical and safety-critical attack surface for AVs.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-01 in cs.LG).
Categorie arXiv: Robotics (cs.RO) ; Computer Vision and Pattern Recognition (cs.CV); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2608.28778 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T08:13:26.881Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).