  Machine learning (ML) is a rapidly developing area of medicine that uses
significant resources to apply computer science and statistics to medical
issues. ML's proponents laud its capacity to handle vast, complicated, and
erratic medical data. It's common knowledge that attackers might cause
misclassification by deliberately creating inputs for machine learning
classifiers. Research on adversarial examples has been extensively conducted in
the field of computer vision applications. Healthcare systems are thought to be
highly difficult because of the security and life-or-death considerations they
include, and performance accuracy is very important. Recent arguments have
suggested that adversarial attacks could be made against medical image analysis
(MedIA) technologies because of the accompanying technology infrastructure and
powerful financial incentives. Since the diagnosis will be the basis for
important decisions, it is essential to assess how strong medical DNN tasks are
against adversarial attacks. Simple adversarial attacks have been taken into
account in several earlier studies. However, DNNs are susceptible to more risky
and realistic attacks. The present paper covers recent proposed adversarial
attack strategies against DNNs for medical imaging as well as countermeasures.
In this study, we review current techniques for adversarial imaging attacks,
detections. It also encompasses various facets of these techniques and offers
suggestions for the robustness of neural networks to be improved in the future.
