  Deep neural network architectures are considered to be robust to random
perturbations. Nevertheless, it was shown that they could be severely
vulnerable to slight but carefully crafted perturbations of the input, termed
as adversarial samples. In recent years, numerous studies have been conducted
in this new area called "Adversarial Machine Learning" to devise new
adversarial attacks and to defend against these attacks with more robust DNN
architectures. However, almost all the research work so far has been
concentrated on utilising model loss function to craft adversarial examples or
create robust models. This study explores the usage of quantified epistemic
uncertainty obtained from Monte-Carlo Dropout Sampling for adversarial attack
purposes by which we perturb the input to the areas where the model has not
seen before. We proposed new attack ideas based on the epistemic uncertainty of
the model. Our results show that our proposed hybrid attack approach increases
the attack success rates from 82.59% to 85.40%, 82.86% to 89.92% and 88.06% to
90.03% on MNIST Digit, MNIST Fashion and CIFAR-10 datasets, respectively.
