TrustFlip weaponizes consistency-based trust defenses in vehicular collaborative perception by using physical adversarial objects to induce inconsistencies that are misattributed to benign vehicles, leading to their exclusion and reduced system performance.
A cooperative perception environment for traffic operations and control
4 Pith papers cite this work. Polarity classification is still indexing.
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The paper organizes perception attacks on AVs into a new taxonomy, identifies gaps in fusion-aware defenses, and validates one cross-sensor vulnerability with a proof-of-concept simulation.
A new online attack framework manipulates object poses in shared CAV perception data below detection thresholds, propagating errors to cause unsafe trajectory predictions and behaviors in up to 50% of tested scenarios while evading defenses.
Introduces a modular dataset generation pipeline using CARLA and AVstack to produce terabyte-scale ground-truth data for ground, aerial, and infrastructure autonomy in single- and multi-agent setups.
citing papers explorer
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Adversarial Trust Poisoning in Vehicular Collaborative Perception
TrustFlip weaponizes consistency-based trust defenses in vehicular collaborative perception by using physical adversarial objects to induce inconsistencies that are misattributed to benign vehicles, leading to their exclusion and reduced system performance.
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SoK: The Next Frontier in AV Security: Systematizing Perception Attacks and the Emerging Threat of Multi-Sensor Fusion
The paper organizes perception attacks on AVs into a new taxonomy, identifies gaps in fusion-aware defenses, and validates one cross-sensor vulnerability with a proof-of-concept simulation.
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From Stealthy Data Fabrication to Unsafe Driving: Realistic Scenario Attacks on Collaborative Perception
A new online attack framework manipulates object poses in shared CAV perception data below detection thresholds, propagating errors to cause unsafe trajectory predictions and behaviors in up to 50% of tested scenarios while evading defenses.
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Scaling Datasets for Multi-Sensor, Multi-Agent, and Multi-Domain Learning in Autonomous Systems
Introduces a modular dataset generation pipeline using CARLA and AVstack to produce terabyte-scale ground-truth data for ground, aerial, and infrastructure autonomy in single- and multi-agent setups.