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This study introduces a novel approach for integrating sensitive patient information within medical images with minimal impact on their diagnostic quality. Utilizing the mask region-based convolutional neural network for identifying regions of minimal medical significance, the method embeds information using discrete cosine transform-based steganography. The focus is on embedding within "insignificant areas", determined by deep learning models, to ensure image quality and confidentiality are maintained. The methodology comprises three main steps: neural network training for area identification, an embedding process for data concealment, and an extraction process for retrieving embedded information. Experimental evaluations on the CHAOS dataset demonstrate the method's effectiveness, with the model achieving an average intersection over union score of 0.9146, indicating accurate segmentation. Imperceptibility metrics, including peak signal-to-noise ratio, were employed to assess the quality of stego images, with results showing high capacity embedding with minimal distortion. Furthermore, the embedding capacity and payload analysis reveal the method's high capacity for data concealment. The proposed method outperforms existing techniques by offering superior image quality, as evidenced by higher peak signal-to-noise ratio values, and efficient concealment capacity, making it a promising solution for secure medical image handling., (© 2024. The Author(s).)

Watermarking techniques in a wide range of digital media was utilized as a host cover to hide or embed a piece of information message in such a way that it is invisible to a human observer. This study aims to develop an enhanced rapid and blind method for producing a watermarked 3D object using QR code images with high imperceptibility and transparency. The proposed method is based on the spatial domain, and it starts with converting the 3D object triangles from the three-dimensional Cartesian coordinate system to the two-dimensional coordinates domain using the corresponding transformation matrix. Then, it applies a direct modification on the third vertex point of each triangle. Each triangle's coordinates in the 3D object can be used to embed one pixel from the QR code image. In the extraction process, the QR code pixels can be successfully extracted without the need for the original image. The imperceptibly and the transparency performances of the proposed watermarking algorithm were evaluated using Euclidean distance, Manhattan distance, cosine distance, and the correlation distance values. The proposed method was tested under various filtering attacks, such as rotation, scaling, and translation. The proposed watermarking method improved the robustness and visibility of extracting the QR code image. The results reveal that the proposed watermarking method yields watermarked 3D objects with excellent execution time, imperceptibility, and robustness to common filtering attacks., Competing Interests: The authors declare that they have no conflicts of interest to report regarding the present study., (Copyright © 2021 Hanan S. Al-Saadi et al.)

Blockchain technology must have sparked widespread interest, applications associated with data monitoring, banking sectors, computer security, the Internet of Things, and food chemistry to the healthcare sector and cognitive science. The use of multimedia in the healthcare architecture also allows for the storage, processing and transmission of patient information in a wide range of formats such as images, text and audio over the Internet using various smart particles. However, managing large amounts of data, including findings and images of each individual, increases human effort and increases protection risks. In this paper, to address these problems by using IoT in healthcare improves the performance of patient care while lowering costs by efficiently distributing healthcare resources. Nevertheless, various attackers can cause a variety of risks in IoT devices. To avoid these problems, Blockchain technology has been identified as the most effective method for maintaining the secrecy and security of control systems in real-time. This should provide a security architecture for healthcare multimedia content using blockchain technology by producing the hash of every information so that any transition or modification in information, as well as any breaches of medicines, would be evidenced across the whole blockchain platform. [ABSTRACT FROM AUTHOR]