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GUARDIAN: Safety Filtering for Systems with Perception Models Under Adversarial Attack
Safety filtering is an effective method for enforcing constraints in safety-critical systems, but existing methods typically assume perfect state information. This limitation is especially problematic for systems that rely on neural network (NN)-based state estimators, which can be highly sensitive
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