CXRmark: A Watermarking Scheme for Chest X-Rays Using Online Sequential Reduced Kernel ELM

In recent times, the use of chest X-ray (CXR) images in telemedicine has increased exponentially. However, the transfer of these images on an unsecured channel poses a serious threat to their authenticity and copyright protection. To address this problem, a chest X-ray image watermarking scheme (CXRmark) is presented in this paper using an online sequential reduced kernel extreme learning machine (OS-RKELM). U-Net is used to segment the lung area into the region of non-interest (RONI) and region of interest (ROI). An OS-RKELM is trained using the approximation coefficients (obtained by transforming training images using discrete wavelet transform) and the corresponding quantized coefficients. Subsequently, it is used to modulate the approximation coefficients. In order to avoid inadvertent deterioration in the ROI and to achieve high robustness, the proposed watermarking scheme uses different embedding strengths for ROI and RONI. Further, to enhance the security of the watermark, it has been encoded using the logistic chaotic map. To evaluate the performance of the proposed scheme, we experimented with a set of 461 CXR images from a publicly available repository: COVID-19 Image Data Collection. It has been demonstrated that CXRmark results in high perceptual quality of the signed CXRs and is robust to common interference. Further, the reversibility of the proposed CXRmark scheme is demonstrated by recovering the undistorted CXR image from the signed CXR. The comparison of the proposed scheme with state-of-the-art schemes shows that CXRmark outperforms its competitors in terms of imperceptibility and robustness.