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.
Sok: On the semantic ai security in autonomous driving
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MIST detects Trojaned DNN updates by measuring spectral deviations in pre-activation representations against a benign fine-tuning reference, achieving high accuracy across datasets and attacks after a single update.
citing papers explorer
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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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Detecting Trojaned DNNs via Spectral Regression Analysis
MIST detects Trojaned DNN updates by measuring spectral deviations in pre-activation representations against a benign fine-tuning reference, achieving high accuracy across datasets and attacks after a single update.