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arXiv AI By Oscar Mogollon Gutierrez, Fatemeh Ghasemi, Mohammadhossein Homaei, Andres Caro, Mar Avila

Physics-Constrained Digital Twins for Sensor Integrity in Urban Pedestrian Flow: Detecting Stealthy False Data Injection with Conformal Guarantees

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The paper introduces physics‑constrained digital twins for urban pedestrian flow that detect stealthy false data injection attacks. By estimating directed flows on a street graph, assimilating counts with a learned graph‑localized gain, and training against a flow‑conservation residual, the twin combines innovation and residuals for detection. Adaptive conformal calibration sets alarm thresholds, and the authors quantify the attack margin—showing a 0.54 reduction in worst‑case corruption for a single compromised device and 0.19 when a third of the fleet is compromised, highlighting the benefit of conservation laws over mere locality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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