  Machine-learning models can be fooled by adversarial examples, i.e.,
carefully-crafted input perturbations that force models to output wrong
predictions. While uncertainty quantification has been recently proposed to
detect adversarial inputs, under the assumption that such attacks exhibit a
higher prediction uncertainty than pristine data, it has been shown that
adaptive attacks specifically aimed at reducing also the uncertainty estimate
can easily bypass this defense mechanism. In this work, we focus on a different
adversarial scenario in which the attacker is still interested in manipulating
the uncertainty estimate, but regardless of the correctness of the prediction;
in particular, the goal is to undermine the use of machine-learning models when
their outputs are consumed by a downstream module or by a human operator.
Following such direction, we: \textit{(i)} design a threat model for attacks
targeting uncertainty quantification; \textit{(ii)} devise different attack
strategies on conceptually different UQ techniques spanning for both
classification and semantic segmentation problems; \textit{(iii)} conduct a
first complete and extensive analysis to compare the differences between some
of the most employed UQ approaches under attack. Our extensive experimental
analysis shows that our attacks are more effective in manipulating uncertainty
quantification measures than attacks aimed to also induce misclassifications.
