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Explaining RADAR features for detecting spoofing attacks in Connected Autonomous Vehicles
We present a model that explains \textit{certainty} and \textit{uncertainty} in sensor input -- a missing characteristic in data collection.
SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability
Interpretability, trustworthiness, and usability are key considerations in high-stake security applications, especially when utilizing deep learning models.
Invisible Reflections: Leveraging Infrared Laser Reflections to Target Traffic Sign Perception
We evaluate the effectiveness of the ILR attack with real-world experiments against two major traffic sign recognition architectures on four IR-sensitive cameras.
PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysis
However, we observe that both model predictions and feature attributions for input samples are sensitive to noise.
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