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Nowadays, we are more and more reliant on Deep Learning (DL) models and thusit is essential to safeguard the security of these systems. This paper exploresthe security issues in Deep Learning and analyses, through the use ofexperiments, the way forward to build more resilient models. Experiments areconducted to identify the strengths and weaknesses of a new approach to improvethe robustness of DL models against adversarial attacks. The results showimprovements and new ideas that can be used as recommendations for researchersand practitioners to create increasingly better DL algorithms.

Improving Deep Learning Model Robustness Against Adversarial Attack by Increasing the Network Capacity