  Nowadays, we are more and more reliant on Deep Learning (DL) models and thus
it is essential to safeguard the security of these systems. This paper explores
the security issues in Deep Learning and analyses, through the use of
experiments, the way forward to build more resilient models. Experiments are
conducted to identify the strengths and weaknesses of a new approach to improve
the robustness of DL models against adversarial attacks. The results show
improvements and new ideas that can be used as recommendations for researchers
and practitioners to create increasingly better DL algorithms.
