  Deep neural network (DNN) 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, which
are termed as adversarial samples. In recent years, numerous studies have been
conducted to increase the reliability of DNN models by distinguishing
adversarial samples from regular inputs. In this work, we explore and assess
the usage of 2 different groups of metrics in detecting adversarial samples:
the ones which are based on the uncertainty estimation using Monte-Carlo
Dropout Sampling and the ones which are based on closeness measures in the
subspace of deep features extracted by the model. We also introduce a new
feature for adversarial detection, and we show that the performances of all
these metrics heavily depend on the strength of the attack being used.
