确保医疗保健数据完整性:使用自主人工智能方法检测深度假冒
IEEE journal of biomedical and health informatics
|July 9, 2025
概括
一个新的动态深度假冒检测框架,DProm,使用视觉提示调来自适应地保护医疗保健数据. 这种方法可确保在不断发展的智能医疗保健系统中对被操纵的医疗图像和记录进行可靠的检测.
科学领域:
- 医疗保健中的人工智能
- 网络安全 网络安全
- 医学成像分析 医学成像分析
背景情况:
- 深度假冒技术的发展对医疗保健数据的完整性构成重大风险,包括医学成像和电子健康记录 (EHR).
- 自主人工智能在智能医疗保健中日益集成,需要先进的方法来对抗深度假冒的滥用数据操纵或假冒.
研究的目的:
- 引入DProm,这是一个动态的深度假冒检测框架,旨在为医疗保健人工智能系统的适应性和高效的安全性而设计.
- 解决静态检测模型在适应新型深度假冒技术方面的局限性.
主要方法:
- DProm使用预先训练的Swin变压器进行视觉提示调 (VPT),以高效地适应新的数据分布.
- 该框架微调一小组视觉提示,最大限度地减少计算和存储开销.
主要成果:
- 在静态交叉数据集评估和动态场景中,DProm 实现了最先进的性能.
- 该框架在多样化和不断变化的数据分布中展示了强大的检测能力.
结论:
- DProm提供了一个可扩展,可适应和资源高效的解决方案,用于医疗保健中的深度假冒检测.
- 该框架提高了智能医疗保健应用中的自主AI的安全性和可靠性,促进了更安全的医疗数据处理.
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