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.
Pixor: Real-time 3d object detection from point clouds,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
L-PCN exploits spatial locality in point cloud networks via octree partitioning into islands and intra-island hub scheduling, delivering 55-94% less feature fetching, 45-81% less computation, and 1.2-3.2x additional speedup on FPGA prototypes.
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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L-PCN: A Point Cloud Accelerator Exploiting Spatial Locality through Octree-based Islandization
L-PCN exploits spatial locality in point cloud networks via octree partitioning into islands and intra-island hub scheduling, delivering 55-94% less feature fetching, 45-81% less computation, and 1.2-3.2x additional speedup on FPGA prototypes.