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

该项目旨在发现支持深度学习的医学成像诊断工具针对对抗性攻击的网络漏洞，并开发防御方法，以追求安全的医疗保健人工智能。人工智能技术，特别是深度学习，在医疗领域取得了显著的成功。新的对抗性攻击对医疗人工智能诊断工具的网络安全构成了新的威胁，但人们对这种威胁的特征和行为知之甚少。虽然人工智能工具越来越多地被纳入医疗成像信息学基础设施，但迫切需要获得有关医疗情境驱动的对抗性攻击的网络安全见解，以设计防御这种威胁的解决方案。医疗对抗性攻击可能导致严重后果，包括患者伤害、医疗服务提供者的责任以及其他伦理问题或犯罪。必须研究这一新出现的网络安全问题，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学基础设施提供安全性评估和保护措施，并为研究人员和监管机构研究人工智能引发的网络安全科学和工程问题奠定了基础。这项研究推进了安全人工智能医疗系统的科学发现、临床部署和实际应用，最终使患者护理、公众和整个社会受益。本研究的技术目标是研究生成对抗网络生成的医学成像对抗攻击的机制，分析人工智能诊断系统在这种攻击下的行为，并开发各种防御策略和方法。生成对抗网络模型被定制为通过在不同分辨率的数字乳房X线照片图像中“插入”或“移除”恶性病变来生成医疗背景激励的对抗样本，同时保持操纵的图像在视觉上与真实图像不可见。四个代表性的防御方法，包括计算算法和人类专家知识相结合的策略，检查防御对抗性攻击。该项目提供算法、教育材料和关键见解，以支持医学人工智能网络安全领域沿着进一步研究活动。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.