A Review of Malicious Altering Healthcare Imagery using Artificial Intelligence

Fadheela Hussain, Riadh Ksantini, M. Hammad
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引用次数: 1

Abstract

During the second half of 2020, healthcare is and has been the number one target for cybercrime, enormous amount of cyberattacks on hospitals and health systems increased, and specialists trust there are more to come. Attackers who can get the way to reach the electronic health record would exploit it and will use it for their own interest like deal or vend it on the underground economy, hostage the systems and the sensitive data, that has a significant impact on operations. This review tried to analyze how cyber attacker employ Generative Adversarial Networks (GANs) to alter the evidences of patient's medical conditions from image scans and reports. Cyber attacker has different purposes in order to obstruct a political applicant, lockup investigations, obligate insurance scam, execute an act of violence, or even commit homicide. Numerous correlated works constructed on gan in medical images practices had been reviews in the period between 2000 to 2021. Many papers showed how hospital system, physicians and radiology's specialists and the most recent researches showed an extremely exposed to different types of intrusion gan attacks.
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利用人工智能恶意改变医疗保健图像的综述
在2020年下半年,医疗保健一直是网络犯罪的头号目标,针对医院和医疗系统的大量网络攻击有所增加,专家们相信未来还会有更多的网络攻击。能够接触到电子健康记录的攻击者会利用它,并将其用于自己的利益,比如在地下经济中交易或出售它,劫持系统和敏感数据,这对运营有重大影响。本综述试图分析网络攻击者如何使用生成对抗网络(gan)来改变来自图像扫描和报告的患者医疗状况证据。网络攻击者有不同的目的,可以阻挠政治申请者,锁定调查,强制保险诈骗,实施暴力行为,甚至杀人。在2000年至2021年期间,对医学图像实践中基于gan的许多相关工作进行了回顾。许多论文显示,医院系统、医生和放射科专家以及最近的研究表明,一个极端暴露于不同类型的入侵器官攻击。
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