CICI: SIVD: Discover and defend cyber vulnerabilities of deep learning medical diagnosis models to adversarial attacks

CICI：SIVD：发现并防御深度学习医疗诊断模型针对对抗性攻击的网络漏洞

• 批准号: 2115082
• 负责人: Shandong Wu
• 金额: $ 49.93万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2115082&HistoricalAwards=false
• 关键词: CICI; SIVD; Discover; defend; cyber

This project aims to discover cyber vulnerabilities of deep learning-enabled medical imaging diagnosis tools against adversarial attacks and to develop defensive approaches in pursuit of safe artificial intelligence for healthcare. Artificial intelligence technologies, especially deep learning, have achieved remarkable success in the medical domain. Newly advanced adversarial attacks pose a new threat to cybersecurity of medical artificial intelligence diagnosis tools, but little is known about the characteristics and behaviors of this threat. While artificial intelligence tools are increasingly being incorporated in medical imaging informatics infrastructures, it is imminent to gain cybersecurity insights on medical context-motivated adversarial attacks for designing solutions to defend this threat. Medical adversarial attacks may lead to serious consequences including patient harm, liability of healthcare providers, and other ethical issues or crimes. It is imperative to study this emerging cybersecurity issue to mitigate the potential consequences and to ensure the safety of health care. This study contributes to providing safety evaluation and protective measures to medical imaging-based artificial intelligence diagnosis devices and clinical informatics infrastructures, and it sets the stage for researchers and regulatory agencies to investigate artificial intelligence-induced cybersecurity science and engineering issues in the medical domain. This study advances scientific discovery, clinical deployment, and practical applications of safe artificial intelligence medical systems, ultimately benefiting patient care, the general public, and society at large. The technical goal of this study is to investigate mechanisms of generative adversarial network-generated medical imaging adversarial attacks, analyze behaviors of an artificial intelligence diagnosis system under such attacks, and develop various defensive strategies and methods. Generative adversarial network models are customized to generate medical context-motivated adversarial samples by “inserting” or “removing” malignant lesions in a varying resolution of digital mammogram images while maintaining the manipulated images to be visually imperceptible to true images. Four representative defensive methods, including the strategy of combining computational algorithms and human expert knowledge, are examined for defending against adversarial attacks. This project contributes algorithms, educational materials, and critical insights to bolster further research activities along the line of medical artificial intelligence cybersecurity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在发现针对对抗性攻击的深度学习医学成像诊断工具的网络脆弱性，并开发防御方法，以追求安全的人工智能来进行医疗保健。人工智能技术，尤其是深度学习，在医疗领域取得了巨大的成功。新高级的对抗攻击对医学人工智能诊断工具的网络安全构成了新的威胁，但对这种威胁的特征和行为知之甚少。尽管人工智能工具越来越多地纳入医学成像信息基础设施中，但即将获得对医学环境动机动机的对抗性攻击的网络安全见解，以设计解决这一威胁的解决方案。医疗对抗攻击可能会导致严重的后果，包括患者伤害，医疗保健提供者的责任以及其他道德问题或犯罪。必须研究这个新兴的网络安全问题，以减轻潜在的后果并确保医疗保健的安全。这项研究有助于为基于医学成像的人工智能诊断设备和临床信息基础设施提供安全评估和保护措施，并为研究人员和监管机构奠定了阶段，以调查人工智能诱导的网络安全性科学和医疗领域中的工程问题。这项研究促进了科学发现，临床部署以及安全人工智能医疗系统的实际应用，最终使患者护理，公众和整个社会受益。这项研究的技术目标是研究通用对抗网络生成的医学成像对抗性攻击的机制，分析此类攻击下人工智能诊断系统的行为，并制定各种防御策略和方法。定制通用的对抗网络模型，以通过“插入”或“删除”恶性病变在数字乳房X线图图像的不同分辨率中“插入”或“删除”恶性病变，同时维护操纵图像以视觉上对真实图像的视觉构成。检查了四种代表性的防御方法，包括结合计算算法和人类专家知识的策略，以防御对抗攻击。该项目贡献了算法，教育材料和关键见解，以沿着医学人工智能网络安全局来增强进一步的研究活动。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的评估标准来评估通过评估来获得的支持。