You have to understand that neural networks are trained to perform a task. With a non-trained network you cannot measure disparity.
If you would have, for example, a classifier, then you can compute a loss which measure the difference between two image in terms of how likely are they to belong to the same class (or whatever loss you use).
NN recognize patters, difficult patters. But you need to “teach” which samples are closer and which samples are farther in terms of your criteria.
Anyway the most closer thing you are looking for is metric learning, siamese networks with contrastive loss and hinge loss. In which NN learn to set pairs of samples closer of further, and then you can compare latent spaces to measure disparity. Anyway you will have to define which “closer” means for you