ECDSA-based tamper detection in medical data using a watermarking technique

Rupa Ch , Naga Vivek K , Gautam Srivastava , Reddy Gadekallu
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引用次数: 0

Abstract

Telemedicine is a form of healthcare delivery that employs communication technology to provide medical care to patients remotely. The use of telemedicine has seen a significant increase in recent years, presenting challenges such as patient privacy, data security, the need for reliable communication technology, and the potential for misdiagnosis without a physical examination. Digital Watermarking can assist in addressing such issues by incorporating a unique identifier into an image that can be used to authenticate its validity. To tackle these issues, this study proposes a robust digital watermarking approach tailored to brain medical images, combining hashing, the Elliptic Curve Digital Signature Algorithm (ECDSA), and the Integer Wavelet Transform-Discrete Cosine Transform (IWT-DCT). This method utilizes the Secure Hash Algorithm (SHA-256) to first segment the brain's Region of Interest (RoI). Subsequently, the hashed RoI, along with an ECDSA signature, is embedded into the high-frequency sub-bands of the medical image using IWT-DCT. The embedding process strategically alters the coefficients of the high-frequency sub-bands to accommodate the signature while minimizing perceptual distortion. The technique leverages the robustness of transformed-domain image watermarking techniques against various attacks and combines it with SHA-256 for integrity and ECDSA for authentication purposes. The results demonstrate that the suggested approach is robust to a variety of image processing techniques, including noise addition, filtering, and compression while maintaining high levels of imperceptibility. Key metrics such as the Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM) were used to evaluate performance. The suggested strategy exhibited a substantial improvement over existing methods. The PSNR increased to 68.67, indicating higher image quality, while the MSE reduced to 0.96, demonstrating closer pixel values to the original image. Moreover, the SSIM reached 0.98, denoting a nearly perfect resemblance between the watermarked and original images. The suggested approach also demonstrated quick embedding and extraction speeds, as well as tamper detection capabilities.

利用水印技术在医疗数据中进行基于 ECDSA 的篡改检测
远程医疗是一种利用通信技术为患者提供远程医疗服务的医疗保健方式。近年来,远程医疗的使用大幅增加,但也带来了一些挑战,如病人隐私、数据安全、对可靠通信技术的需求,以及在没有身体检查的情况下误诊的可能性。数字水印技术可以在图像中加入独特的标识符,用来验证图像的有效性,从而帮助解决这些问题。为解决这些问题,本研究提出了一种专为脑部医学图像定制的稳健数字水印方法,将散列、椭圆曲线数字签名算法(ECDSA)和整数小波变换-离散余弦变换(IWT-DCT)结合起来。这种方法利用安全散列算法(SHA-256)首先分割大脑的感兴趣区(RoI)。随后,利用 IWT-DCT 将散列 RoI 和 ECDSA 签名嵌入医学图像的高频子带中。嵌入过程会策略性地改变高频子带的系数,以容纳签名,同时最大限度地减少感知失真。该技术利用了变换域图像水印技术对各种攻击的鲁棒性,并将其与 SHA-256 结合以实现完整性,与 ECDSA 结合以实现验证目的。结果表明,所建议的方法对各种图像处理技术(包括噪声添加、滤波和压缩)都很稳健,同时还能保持较高的不可感知性。峰值信噪比(PSNR)、平均平方误差(MSE)和结构相似性指数(SSIM)等关键指标被用来评估性能。与现有方法相比,建议的策略有了很大改进。PSNR 升至 68.67,表明图像质量更高,而 MSE 降至 0.96,表明像素值更接近原始图像。此外,SSIM 达到了 0.98,表明水印图像与原始图像几乎完全相似。所建议的方法还展示了快速的嵌入和提取速度,以及篡改检测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
13.80
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