  In this paper, targeted fooling of high performance image classifiers is
achieved by developing two novel attack methods. The first method generates
universal perturbations for target classes and the second generates image
specific perturbations. Extensive experiments are conducted on MNIST and
CIFAR10 datasets to provide insights about the proposed algorithms and show
their effectiveness.
