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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. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging is a medical imaging technique to create comprehensive images of the tissues and organs in the body. This study presents an advanced approach for storing and compressing neuroimaging informatics technology initiative files, a standard format in magnetic resonance imaging. It is designed to enhance telemedicine services by facilitating efficient and high-quality communication between healthcare practitioners and patients. The proposed downsampling approach begins by opening the neuroimaging informatics technology initiative file as volumetric data and then planning it into several slice images. Then, the quantization hiding technique will be applied to each of the two consecutive slice images to generate the stego slice with the same size. This involves the following major steps: normalization, microblock generation, and discrete cosine transformation. Finally, it assembles the resultant stego slice images to produce the final neuroimaging informatics technology initiative file as volumetric data. The upsampling process, designed to be completely blind, reverses the downsampling steps to reconstruct the subsequent image slice accurately. The efficacy of the proposed method was evaluated using a magnetic resonance imaging dataset, focusing on peak signal-to-noise ratio, signal-to-noise ratio, structural similarity index, and Entropy as key performance metrics. The results demonstrate that the proposed approach not only significantly reduces file sizes but also maintains high image quality. [ABSTRACT FROM AUTHOR]

Cloud data sharing allows users to access data stored on the web object. Security and protecting cloud data sharing form different attacks is recently considered one of the most challenges. In this paper, we are proposing a framework for cloud data sharing protection against unauthorized access. The proposed framework is based on DNA-proxy re-encryption. Firstly, three keys are generated for the owner, proxy and the user who need to access the data. Then, the owner stores his data encrypted on the cloud using his key. If the user wants to access this data then he can access it via the proxy after re-encrypting using the second generated key for the proxy. Finally, the user can decrypt the re-encrypted data with the third generated key. The framework was implemented using various plaintext files and real DNA sequences. The experimental results show that the framework has an outstanding performance in terms of execution time. [ABSTRACT FROM AUTHOR]

In this paper, a framework to hide privacy in video is proposed based on data hiding principals. A novel data hiding technique is proposed and implemented to hide the original frame into the in-painted one. The proposed hiding technique is carried out in the discrete wavelet transform domain of the cover video. The proposed technique is embedding video into video. Furthermore, the proposed data hiding method can blindly reconstruct the original frame from the fake one. Experimental results showed that the proposed method can successfully hide the complete frames of the original video into their corresponding in-painted ones that are as large as themselves. Simple visual inspection of the results showed that the quality of the stego-frames maintain very high (above 45 dB) while providing an acceptable visual quality for the retrieved original frames. [ABSTRACT FROM AUTHOR]