Scalability and performance comparison.

Medical image security is paramount in the digital era but remains a significant challenge. This paper introduces an innovative zero-watermarking methodology tailored for medical imaging, ensuring robust protection without compromising image quality. We utilize Sped-up Robust features for high-precision feature extraction and singular value decomposition (SVD) to embed watermarks into the frequency domain, preserving the original image’s integrity. Our methodology uniquely encodes watermarks in a non-intrusive manner, leveraging the robustness of the extracted features and the resilience of the SVD approach. The embedded watermark is imperceptible, maintaining the diagnostic value of medical images. Extensive experiments under various attacks, including Gaussian noise, JPEG compression, and geometric distortions, demonstrate the methodology’s superior performance. The results reveal exceptional robustness, with high Normalized Correlation (NC) and Peak Signal-to-noise ratio (PSNR) values, outperforming existing techniques. Specifically, under Gaussian noise and rotation attacks, the watermark retrieved from the encrypted domain maintained an NC value close to 1.00, signifying near-perfect resilience. Even under severe attacks such as 30% cropping, the methodology exhibited a significantly higher NC compared to current state-of-the-art methods.

数字时代下，医学图像安全至关重要，但仍面临诸多严峻挑战。本文提出一种专为医学影像场景设计的创新型零水印（zero-watermarking）方法，可在不牺牲图像质量的前提下实现可靠保护。本方法采用加速鲁棒特征（Sped-up Robust Features）实现高精度特征提取，并结合奇异值分解（SVD）将水印嵌入频域，以保障原始图像的完整性。该方法以非侵入式的方式完成水印的唯一编码，充分利用提取特征的鲁棒性与SVD方法的抗攻击能力。嵌入的水印不可感知，不会损害医学图像的诊断价值。本文在高斯噪声、JPEG压缩、几何失真等多种攻击场景下开展了大量实验，验证了该方法的优异性能。实验结果显示该方法具备极强的鲁棒性，归一化相关系数（Normalized Correlation, NC）与峰值信噪比（Peak Signal-to-noise ratio, PSNR）均处于较高水平，整体性能优于现有同类技术。具体而言，在高斯噪声与旋转攻击场景下，从加密域中提取的水印NC值接近1.00，表明其抗攻击能力近乎完美。即便在30%裁剪这类高强度攻击下，本方法的NC值仍显著优于当前最先进技术。