  Testing remains the primary method to evaluate the accuracy of neural network
perception systems. Prior work on the formal verification of neural network
perception models has been limited to notions of local adversarial robustness
for classification with respect to individual image inputs. In this work, we
propose a notion of global correctness for neural network perception models
performing regression with respect to a generative neural network with a
semantically meaningful latent space. That is, against an infinite set of
images produced by a generative model over an interval of its latent space, we
employ neural network verification to prove that the model will always produce
estimates within some error bound of the ground truth. Where the perception
model fails, we obtain semantically meaningful counter-examples which carry
information on concrete states of the system of interest that can be used
programmatically without human inspection of corresponding generated images.
Our approach, Generate and Verify, provides a new technique to gather insight
into the failure cases of neural network perception systems and provide
meaningful guarantees of correct behavior in safety critical applications.
