Approximate Conformance Verification of Deep Neural Networks

We consider the problem of approximate conformance checking on deep neural networks. More precisely, given two neural networks and a conformance bound \(\epsilon \) , we need to check if the neural network outputs are within \(\epsilon \) given the same inputs from the input set. Our approach reduces the approximate conformance checking problem to a reachability analysis problem using transformations of neural networks. We provide experimental comparison of \(\epsilon \) -conformance checking based on our approach using various reachability analysis tools as well as other alternate \(\epsilon \) -conformance checking algorithms. We illustrate the benefits of our approach as well as identify reachability analysis tools that are conducive for conformance checking.