LRIA, Department of Computer Science, University of Science and Technology Houari Boumediene (USTHB), B.P. 32, El Alia, 16111 Bab Ezzouar, Algiers, Algeria.
Department of Computer Science, University of Algiers 1, Algiers, Algeria.
J Med Syst. 2024 Oct 19;48(1):98. doi: 10.1007/s10916-024-02110-x.
The exchange of medical images and patient data over the internet has attracted considerable attention in the past decade, driven by advancements in communication and health services. However, transferring confidential data through insecure channels, such as the internet, exposes it to potential manipulations and attacks. To ensure the authenticity of medical images while concealing patient data within them, this paper introduces a high-capacity and reversible fragile watermarking model in which an authentication watermark is initially generated from the cover image and merged with the patient's information, photo, and medical report to form the global watermark. This watermark is subsequently encrypted using the chaotic Chen system technique, enhancing the model's security and ensuring patient data confidentiality. The cover image then undergoes a Discrete Fourier Transform (DFT) and the encrypted watermark is inserted into the frequency coefficients using a new embedding technique. The experimental results demonstrate that the proposed method achieves great watermarked image quality, with a PSNR exceeding 113 dB and an SSIM close to 1, while maintaining a high embedding capacity of 3 BPP (Bits Per Pixel) and offering perfect reversibility. Furthermore, the proposed model demonstrates high sensitivity to attacks, successfully detecting tampering in all 18 tested attacks, and achieves nearly perfect watermark extraction accuracy, with a Bit Error Rate (BER) of 0.0004%. This high watermark extraction accuracy is crucial in our situation where patient data need to be retrieved from the watermarked images with almost no alteration.
在过去的十年中，随着通信和医疗服务技术的进步，通过互联网交换医学图像和患者数据引起了相当大的关注。然而，通过互联网等不安全的渠道传输机密数据会使其面临潜在的操纵和攻击。为了在隐藏图像中患者数据的同时确保医学图像的真实性，本文提出了一种大容量且可恢复的脆弱水印模型，该模型首先从原始图像生成认证水印，并将其与患者的信息、照片和医疗报告合并，形成全局水印。然后使用混沌 Chen 系统技术对该水印进行加密，从而提高模型的安全性并确保患者数据的机密性。接下来，原始图像将经历离散傅里叶变换（DFT），并且使用新的嵌入技术将加密水印插入到频率系数中。实验结果表明，所提出的方法实现了良好的水印图像质量，峰值信噪比（PSNR）超过 113dB，结构相似性（SSIM）接近 1，同时保持了 3 位/像素（BPP）的高嵌入容量，并提供了完美的可恢复性。此外，所提出的模型对攻击具有高度敏感性，成功检测到所有 18 种测试攻击中的篡改行为，并且实现了几乎完美的水印提取精度，误码率（BER）为 0.0004%。在我们的情况下，需要从水印图像中检索患者数据，且几乎不进行任何修改，因此这种高水印提取精度至关重要。
