深度学习隐形图形用于大数据安全,使用开始架构的挤压和激发
Bini M Issac1, S N Kumar2, Sherin Zafar3
1Dept. of Computer Science & Engineering, Amal Jyothi College of Engineering, APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, 695 016, India.
Scientific reports
|August 25, 2025
概括
这项研究引入了一个新的深度学习框架,用于安全的医疗图像隐形图像,确保远程医疗应用的数据完整性和实时性能. 这种方法有效地嵌入和重建敏感的医疗数据,
科学领域:
- 计算机科学
- 医学成像
- 网络安全
背景情况:
- 由于远程医疗和数字法医的巨大数据增长, 敏感医疗数据的安全传输至关重要.
- 传统的稳定学方法在诊断完整性和对抗噪音和变化的稳定性方面扎.
研究的目的:
- 开发一种基于深度学习的新型隐形图像框架,用于安全传输医疗图像.
- 解决传统方法在维持诊断完整性和稳定性的局限性.
主要方法:
- 提出了一个结合挤压激发 (SE) 块,Inception模块和剩余连接的框架.
- 编码器使用扩展卷曲和SE关注嵌入秘密医疗图像到封面图像.
- 解码器使用基于Inception的残留和多尺度特征提取进行重建.
主要成果:
- 该模型在MRI和OCT数据集上实现了高峰信号噪声比 (PSNR) 值 (39.02,38.75) 和结构相似度指数 (SSIM) 值 (0.9757).
- 显示了最小的视觉扭曲, 证实了石学方法的有效性.
- 设计用于NVIDIA Jetson TX2的实时,低功耗部署,用于边缘医疗应用.
结论:
- 开发的深度学习框架为隐私敏感环境提供安全,高容量的隐私解决方案.
- 该模型的实时性能和稳定性使其适用于远程医疗和数字法医的实际应用.
- 这项研究促进了医疗数据的安全处理,保证了机密性和诊断质量.
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