  While state-of-the-art Deep Neural Network (DNN) models are considered to be
robust to random perturbations, it was shown that these architectures are
highly vulnerable to deliberately crafted perturbations, albeit being
quasi-imperceptible. These vulnerabilities make it challenging to deploy DNN
models in security-critical areas. In recent years, many research studies have
been conducted to develop new attack methods and come up with new defense
techniques that enable more robust and reliable models. In this work, we
explore and assess the usage of different type of metrics for detecting
adversarial samples. We first leverage the usage of moment-based predictive
uncertainty estimates of a DNN classifier obtained using Monte-Carlo Dropout
Sampling. And we also introduce a new method that operates in the subspace of
deep features extracted by the model. We verified the effectiveness of our
approach on a range of standard datasets like MNIST (Digit), MNIST (Fashion)
and CIFAR-10. Our experiments show that these two different approaches
complement each other, and the combined usage of all the proposed metrics
yields up to 99 \% ROC-AUC scores regardless of the attack algorithm.
