  Adversarial examples are of wide concern due to their impact on the
reliability of contemporary machine learning systems. Effective adversarial
examples are mostly found via white-box attacks. However, in some cases they
can be transferred across models, thus enabling them to attack black-box
models. In this work we evaluate the transferability of three adversarial
attacks - the Fast Gradient Sign Method, the Basic Iterative Method, and the
Carlini & Wagner method, across two classes of models - the VGG class(using
VGG16, VGG19 and an ensemble of VGG16 and VGG19), and the Inception
class(Inception V3, Xception, Inception Resnet V2, and an ensemble of the
three). We also outline the problems with the assessment of transferability in
the current body of research and attempt to amend them by picking specific
"strong" parameters for the attacks, and by using a L-Infinity clipping
technique and the SSIM metric for the final evaluation of the attack
transferability.
