In this article, we have proposed a blind, fragile and Region of Interest (ROI) lossless medical image watermarking (MIW) technique, providing an all-in-one solution tool to various medical data distribution and management issues like security, content authentication, safe archiving, controlled access retrieval, and captioning. The proposed scheme combines lossless data compression and encryption technique to embed electronic health record (EHR)/DICOM metadata, image hash, indexing keyword, doctor identification code and tamper localization information in the medical images. Extensive experiments (both subjective and objective) were carried out to evaluate performance of the proposed MIW technique. The findings offer suggestive evidence that the proposed MIW scheme is an effective all-in-one solution tool to various issues of medical information management domain. Moreover, given its relative simplicity, the proposed scheme can be applied to the medical images to serve in many medical applications concerned with privacy protection, safety, and management.
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http://dx.doi.org/10.1016/j.cmpb.2013.05.027 | DOI Listing |
iScience
December 2024
Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.
As pioneers of next-generation watermarking technologies, quantum methods offer advanced solutions for securing digital text copyright. Quantum text representation is a prerequisite for realizing quantum watermarking. Thus we propose a generalized quantum text representation (GQTR) model for English text.
View Article and Find Full Text PDFNeural Netw
December 2024
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China. Electronic address:
Superimposing visible watermarks on images is an efficient way to indicate ownership and prevent potential unauthorized use. Visible watermark removal technology is receiving increasing attention from researchers due to its ability to enhance the robustness of visible watermarks. In this paper, we propose MNet, a novel multi-scale network for visible watermark removal.
View Article and Find Full Text PDFSci Data
November 2024
National Center for High-Performance Computing, Hsinchu, Taiwan.
Digital documents play a crucial role in contemporary information management. However, their quality can be significantly impacted by various factors such as hand-drawn annotations, image distortion, watermarks, stains, and degradation. Deep learning-based methods have emerged as powerful tools for document enhancement.
View Article and Find Full Text PDFNeural Netw
January 2025
National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd., Taipei, Taiwan. Electronic address:
Both image denoising and watermark removal aim to restore a clean image from an observed noisy or watermarked one. The past research consists of the non-learning type with limited effectiveness or the learning types with limited interpretability. To address these issues simultaneously, we propose a method to deal with both the image-denoising and watermark removal tasks in a unified approach.
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