Measuring the transfer of adversarial examples over ensemble networks of the same architecture (VGG-16 and LeNet-5) Testing the theory of whether adversarial examples take on arbitrary labels under the same models trained w/ different initializations.
models
contains an abstract base class implementation of an ensemble network, as well as the specific instantiations for VGG-16 on Cifar and LeNet on MNIST.
models/nn
contains an implementation of LeNet-5 and VGG-16 - shouldn't be too hard to add other models.
Re-implementation from Mathematica, found here: https://github.com/Nico-Adamo/WSC2019-Colorful-Fraud