  Deep neural networks (DNN), while becoming the driving force of many novel
technology and achieving tremendous success in many cutting-edge applications,
are still vulnerable to adversarial attacks. Differentiable neural computer
(DNC) is a novel computing machine with DNN as its central controller operating
on an external memory module for data processing. The unique architecture of
DNC contributes to its state-of-the-art performance in tasks which requires the
ability to represent variables and data structure as well as to store data over
long timescales. However, there still lacks a comprehensive study on how
adversarial examples affect DNC in terms of robustness. In this paper, we
propose metamorphic relation based adversarial techniques for a range of tasks
described in the natural processing language domain. We show that the
near-perfect performance of the DNC in bAbI logical question answering tasks
can be degraded by adversarially injected sentences. We further perform
in-depth study on the role of DNC's memory size in its robustness and analyze
the potential reason causing why DNC fails. Our study demonstrates the current
challenges and potential opportunities towards constructing more robust DNCs.
