I'm not an expert in any sense, just a curious bystander. Assuming that the ratio of perturbations causing misclassifications to ones that don't is extremely lo

I'm not an expert in any sense, just a curious bystander. Assuming that the ratio of perturbations causing misclassifications to ones that don't is extremely low, couldn't you perturb the image in the time dimension, such that "dog" misclassifications would be odd blips in an otherwise continuous "cat" signal, with some sort of smoothing applied that would average those blips away? And in fact wouldn't that be the default case in some real world implementations, such as biological NNs or driverless car ones? The input to the NN would be a live video feed captured via light focused on some kind of CCD, which is going to be inherently noisy.