  Machine learning models are vulnerable to adversarial examples. An adversary
modifies the input data such that humans still assign the same label, however,
machine learning models misclassify it. Previous approaches in the literature
demonstrated that adversarial examples can even be generated for the remotely
hosted model. In this paper, we propose a Siamese network based approach to
generate adversarial examples for a multiclass target CNN. We assume that the
adversary do not possess any knowledge of the target data distribution, and we
use an unlabeled mismatched dataset to query the target, e.g., for the
ResNet-50 target, we use the Food-101 dataset as the query. Initially, the
target model assigns labels to the query dataset, and a Siamese network is
trained on the image pairs derived from these multiclass labels. We learn the
\emph{adversarial perturbations} for the Siamese model and show that these
perturbations are also adversarial w.r.t. the target model. In experimental
results, we demonstrate effectiveness of our approach on MNIST, CIFAR-10 and
ImageNet targets with TinyImageNet/Food-101 query datasets.
