Adversarial Attack and Defense Mechanisms in Medical Imaging: A Comprehensive Review

Medical imaging is essential in modern healthcare, allowing for accurate diagnoses and treatment planning. The practice of machine learning algorithms into clinical imaging uses has significantly enhanced diagnostic abilities. In contrast, as technology has forward-looking, new problems have emerged, main among them existence adversarial assaults. This complete research study investigates the background information of adversarial attacks and its rising defense measures on medical image model. Adversarial assaults involve intelligent alterations of input data to change machine learning algorithms, possibly important to misdiagnosis or inaccurate clinical diagnosis. This paper thoroughly analyses many adversarial assault practices precisely tailored for medical imaging based on division, classification and rebuilding of clinical image with different defensive tactic. Also, the research study provides the better path for collect rising counter measures on adversarial attacks posed by threats. These protective measures contain different types of proposals. For example, adversarial practices, input pre-processing and algorithm understandable strategies.