MITS-GAN:保护医疗成像免受与生成对抗网络的改
Giovanni Pasqualino1, Luca Guarnera1, Alessandro Ortis1
1Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, Catania, 95126, Italy.
Computers in biology and medicine
|October 9, 2024
概括
这项研究介绍了MITS-GAN,这是一种使用高斯噪声保护医疗CT扫描免受生成模型改的新方法. 它增强了对恶意攻击的图像安全性,具有不可察觉的干扰.
科学领域:
- 医疗成像医学成像
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 像生成对抗网络 (GAN) 这样的生成模型提供了先进的图像生成功能.
- 恶意使用GAN,特别是在敏感的医学成像中,会带来重大风险.
- 现有的方法来保护医疗图像免受改是不够的.
研究的目的:
- 介绍MITS-GAN,这是一种用于防改医疗图像的新方法,特别是CT扫描.
- 提高医疗图像对抗GAN的对抗性攻击的抵抗力.
- 确保医学成像数据的完整性和可靠性.
主要方法:
- 开发了MITS-GAN,一个引入微调,人类无法感知的干扰输入数据的系统.
- 利用高斯噪声作为对针对CT-GAN架构的各种对抗性攻击的保护措施.
- 专注于破坏攻击者控制的CT-GAN的输出,以防止图像操纵.
主要成果:
- 与现有技术相比,MITS-GAN 显示出更高的抗改性.
- 对CT扫描的实验结果显示,产生了具有微不足道文物的防改图像.
- 拟议的方法有效地抵制恶意改尝试,而不会影响图像质量.
结论:
- MITS-GAN提供了一种强大的解决方案,用于保护医疗图像免受复杂的网络威胁.
- 这种主动的方法支持在医疗保健中负责任和道德地应用生成模型.
- 这项研究为未来医学成像网络安全和对抗防御领域的研究奠定了基础.
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