  The great performance of machine learning algorithms and deep neural networks
in several perception and control tasks is pushing the industry to adopt such
technologies in safety-critical applications, as autonomous robots and
self-driving vehicles. At present, however, several issues need to be solved to
make deep learning methods more trustworthy, predictable, safe, and secure
against adversarial attacks. Although several methods have been proposed to
improve the trustworthiness of deep neural networks, most of them are tailored
for specific classes of adversarial examples, hence failing to detect other
corner cases or unsafe inputs that heavily deviate from the training samples.
  This paper presents a lightweight monitoring architecture based on coverage
paradigms to enhance the model robustness against different unsafe inputs. In
particular, four coverage analysis methods are proposed and tested in the
architecture for evaluating multiple detection logics. Experimental results
show that the proposed approach is effective in detecting both powerful
adversarial examples and out-of-distribution inputs, introducing limited
extra-execution time and memory requirements.
