Proof transfer for fast certification of multiple approximate neural networks

Complete verification of deep neural networks (DNNs) can exactly determine whether the DNN satisfies a desired trustworthy property (e.g., robustness, fairness) on an infinite set of inputs or not. Despite the tremendous progress to improve the ...

We consider the problem of approximate conformance checking on deep neural networks. More precisely, given two neural networks and a conformance bound $ϵ$, we need to check if the neural network outputs are within $ϵ$ given the same inputs from the ...$$$$

Deep Neural Networks (DNNs), extensively used in safety-critical domains, require methods to detect misbehavior and ensure provable specifications. DNN testing encounters limitations in time and coverage, affecting effectiveness. DNN verification ...