  There has been a concurrent significant improvement in the medical images
used to facilitate diagnosis and the performance of machine learning techniques
to perform tasks such as classification, detection, and segmentation in recent
years. As a result, a rapid increase in the usage of such systems can be
observed in the healthcare industry, for instance in the form of medical image
classification systems, where these models have achieved diagnostic parity with
human physicians. One such application where this can be observed is in
computer vision tasks such as the classification of skin lesions in
dermatoscopic images. However, as stakeholders in the healthcare industry, such
as insurance companies, continue to invest extensively in machine learning
infrastructure, it becomes increasingly important to understand the
vulnerabilities in such systems. Due to the highly critical nature of the tasks
being carried out by these machine learning models, it is necessary to analyze
techniques that could be used to take advantage of these vulnerabilities and
methods to defend against them. This paper explores common adversarial attack
techniques. The Fast Sign Gradient Method and Projected Descent Gradient are
used against a Convolutional Neural Network trained to classify dermatoscopic
images of skin lesions. Following that, it also discusses one of the most
popular adversarial defense techniques, adversarial training. The performance
of the model that has been trained on adversarial examples is then tested
against the previously mentioned attacks, and recommendations to improve neural
networks robustness are thus provided based on the results of the experiment.
