Earlier quoted context omitted.
Overfitting has nothing to do with it. See this paper: http://arxiv.org/abs/1412.6572 I believe the original paper tried ensembles and even got the images to work on different networks.
I was not talking about overfitting. I've seen that paper. The original paper asked if images that could fool DBN.a could fool DBN.b. The answer was: certainly not all the time. They used the exact same train set and architecture for DBN.a and DBN.b, just randomly varied initial weights. I think this is too favorable for a comparison with a voting ensemble made with nets with a different architecture, train set and t…
>In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.