Steeg Katharina, Bohrer Evelyn, Schäfer Stefan Benjamin, Vu Viet Duc, Scherberich Jan, Windfelder Anton George, Krombach Gabriele Anja
Department of Diagnostic and Interventional Radiology, University Hospital Giessen, Justus-Liebig-University Giessen, Klinikstraße 33, 35392, Giessen, Germany.
Department of Bioresources, Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Giessen, Germany.
EClinicalMedicine. 2024 Nov 20;78:102930. doi: 10.1016/j.eclinm.2024.102930. eCollection 2024 Dec.
Facial recognition software (FRS) has historically been perceived as lacking the capability to identify individuals from cross-sectional medical images. Utilising such data for identification purposes was considered infeasible due to the substantial computational power and specialised technical expertise it would require. However, recent advancements in accessible artificial intelligence-based (AI-based) software and open-source tools have made these applications widely available and easy to use, raising new privacy concerns.
This proof-of-concept was designed as a cross-sectional study and included participants with a verified online presence. Standard magnetic resonance imaging (MRI) head scans were performed on these participants, from which three-dimensional rendering (3DR) images were created using free and publicly available software. These images were used for face searches by free and publicly available FRS. Different head orientations and hairstyles were applied to the 3DR images to assess whether non-facial features influenced the FRS results. All results were obtained between the 10th of February 2024 and the 1st of March 2024.
Face searches of 3DR images in a database containing over 800 million images from the World Wide Web (WWW) yielded correct matches for 50% of the participants in less than 10 min. The user-friendly software required minimal computational knowledge or resources, making this process broadly accessible. Modifying elements such as hairstyles or the orientation of the 3DR to better resemble actual photographs of the participants improved FRS matches.
Current existing FRS can swiftly and accurately identify individuals from MRI head scans. This poses a significant privacy risk for participants in enrolled clinical trials and highlights the urgent need for improved data protection measures and increased sensitivity to ensure participant confidentiality.
There was no funding source for this study.
面部识别软件(FRS)一直以来被认为缺乏从横断面医学图像中识别个体的能力。由于识别所需的强大计算能力和专业技术知识,利用此类数据进行身份识别被认为是不可行的。然而,基于人工智能的(基于AI的)软件和开源工具的最新进展使这些应用广泛可用且易于使用,引发了新的隐私问题。
本概念验证设计为一项横断面研究,纳入了在网上身份已得到验证的参与者。对这些参与者进行了标准的磁共振成像(MRI)头部扫描,并使用免费的公开可用软件从中创建三维渲染(3DR)图像。这些图像被用于通过免费的公开可用面部识别软件进行面部搜索。对3DR图像应用不同的头部朝向和发型,以评估非面部特征是否会影响面部识别软件的结果。所有结果均在2024年2月10日至2024年3月1日期间获得。
在一个包含来自万维网(WWW)的8亿多张图像的数据库中,对3DR图像进行面部搜索,在不到10分钟的时间内,50%的参与者得到了正确匹配。这款用户友好型软件所需的计算知识或资源极少,使这一过程广泛适用。修改发型或3DR图像的朝向等元素,使其更接近参与者的实际照片,可提高面部识别软件的匹配度。
当前现有的面部识别软件能够快速、准确地从MRI头部扫描中识别个体。这给参与登记临床试验的参与者带来了重大隐私风险,并凸显了迫切需要改进数据保护措施,提高敏感度以确保参与者的保密性。
本研究无资金来源。