Security in medical records is critical to patient privacy and confidentiality. Digital Patient Records (DPR) hold sensitive information that can reveal a patient's health status and history. Their unauthorized access or exposure can lead to severe consequences, including identity theft, discrimination, and medical malpractice. Therefore, ensuring proper security measures is critical in protecting DPR and other medical records from breaches or unauthorized access. In this regard, a robust deep learning-based zero-watermarking approach is presented for authenticating and securing healthcare records. The carrier image is initially visibly marked with the hospital logo to identify ownership and prevent illegal duplication and forgery. The image mark is scrambled by applying the step space-filling curve method for improved security. In the final phase, Alexnet is used to extract the features of visibly marked carrier image. Further, NSST and SVD-based zero watermarking is implemented to conceal the scrambled mark within the features of visibly marked carrier images. It is essential for copyright protection since it establishes ownership while preventing the unauthorized use or dissemination of valuable medical research, images, and reports. The proposed framework has exhibited superior versatility, robustness, and imperceptibility compared to existing techniques with a maximum improvement of 47%.

中文翻译：

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验证和保护医疗记录：基于深度学习的零水印方法

医疗记录的安全对于患者隐私和保密至关重要。数字患者记录 (DPR) 保存可以揭示患者健康状况和病史的敏感信息。未经授权的访问或暴露可能会导致严重后果，包括身份盗窃、歧视和医疗事故。因此，确保适当的安全措施对于保护 DPR 和其他医疗记录免遭破坏或未经授权的访问至关重要。在这方面，提出了一种强大的基于深度学习的零水印方法，用于验证和保护医疗记录。载体图像最初明显地标有医院徽标，以识别所有权并防止非法复制和伪造。采用阶梯空间填充曲线方法对图像标记进行置乱，以提高安全性。在最后阶段，Alexnet用于提取明显标记的载体图像的特征。此外，还实现了基于 NSST 和 SVD 的零水印，以将加扰标记隐藏在可见标记的载体图像的特征内。它对于版权保护至关重要，因为它确立了所有权，同时防止未经授权使用或传播有价值的医学研究、图像和报告。与现有技术相比，所提出的框架表现出卓越的多功能性、鲁棒性和不易察觉性，最大改进为 47%。