As digital images are consistently generated and transmitted online, the unauthorized utilization of these images is an increasing concern that has a significant impact on both security and privacy issues; additionally, the representation of digital images requires a large amount of data. In recent years, an image compression scheme has been widely considered; such a scheme saves on hardware storage space and lowers both the transmission time and bandwidth demand for various potential applications. In this article, we review the various approaches taken to consider joint encryption and compression, assessing both their merits and their limitations. In addition to the survey, we also briefly introduce the most interesting and most often utilized applications of image encryption and evaluation metrics, providing an overview of the various kinds of image encryption schemes available. The contribution made by these approaches is then summarized and compared, offering a consideration of the different technical perspectives. Lastly, we highlight the recent challenges and some potential research directions that could fill the gaps in these domains for both researchers and developers.
Nowadays, the demand for digital images from different intelligent devices and sensors has dramatically increased in smart healthcare. Due to advanced low-cost and easily available tools and software, manipulation of these images is an easy task. Thus, the security of digital images is a serious challenge for the content owners, healthcare communities and researchers against illegal access and fraudulent usage. In this paper, a secure medical image encryption algorithm,
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, based on optimization and the Lorenz system, is proposed for smart healthcare applications. In the first stage, an optimized random sequence (ORS) is generated through directed weighted complex network particle swarm optimization using the genetic algorithm (GDWCN-PSO). This random number matrix and the Lorenz system are adopted to encrypt plain medical images, obtaining the cipher messages with a relationship to the plain images. According to our obtained results, the proposed
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encryption algorithm is effective and resistant to the many attacks on benchmark Kaggle and Open-i datasets. Further, extensive experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art approaches.
Over recent years, the volume of big data has drastically increased for medical applications. Such data are shared by cloud providers for storage and further processing. Medical images contain sensitive information, and these images are shared with healthcare workers, patients, and, in some scenarios, researchers for diagnostic and study purposes. However, the security of these images in the transfer process is extremely important, especially after the COVID-19 pandemic. This paper proposes a secure watermarking algorithm, termed WatMIF, based on multimodal medical image fusion. The proposed algorithm consists of three major parts: the encryption of the host media, the fusion of multimodal medical images, and the embedding and extraction of the fused mark. We encrypt the host media with a key-based encryption scheme. Then, a nonsubsampled contourlet transform (NSCT)-based fusion scheme is employed to fuse the magnetic resonance imaging (MRI) and computed tomography (CT) scan images to generate the fused mark image. Furthermore, the encrypted host media conceals the fused watermark using redundant discrete wavelet transform (RDWT) and randomised singular value decomposition (RSVD). Finally, denoising convolutional neural network (DnCNN) is used to improve the robustness of the WatMIF algorithm. The simulation experiments on two standard datasets were used to evaluate the algorithm in terms of invisibility, robustness, and security. When compared with the existing algorithms, the robustness is improved by 20.14%. Overall, the implementation of proposed watermarking for hiding fused marks and efficient encryption improved the identity verification, invisibility, robustness and security criteria in our WatMIF algorithm.
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