Automation in the area of ship navigation and course planning is adversely affected by oceanic conditions. It leads to deviation from the set course, damages the ship structure, and reduces overall efficiency. Autopilot in ships can keep the ship on course but is unable to choose an efficient path in real-time. Varying wave height is one of the most prominent causes that lead to this inefficiency in the ship’s autopilot system. Current state-of-the-art methods in the domain include building machine and deep learning-based models to estimate wave height. However, the existing systems have several limitations, such as difficulty in handling abrupt non-linear and chaotic variations present in the data, low generalizability, noise sensitivity, and many more. To resolve this issue, the current study proposes a hybrid approach involving a combination of Variable Mode Decomposition and Bidirectional Long Short-Term Memory model (VMD -BiLSTM) and its integration to the ship’s autopilot system. The VMD-based data decomposition enables deep learning models to smoothly and accurately capture observed variational components present in the data, contributing to improved accuracy. Performance comparison with state-of-the-art prediction models validates the efficiency and reliability of the proposed prediction approach.

With the growth and popularity of the utilization of medical images in smart healthcare, the security of these images using watermarks is one of the most recent research topics. This algorithm is based on the joint use of dual watermarking, nature-inspired optimization, and encryption schemes utilizing redundant-discrete wavelet transform (RDWT) and randomized-singular value decomposition (RSVD). The key idea of the proposed method is to embed system encoded media access control (MAC) address in patient’s ID card image via discrete wavelet transform (DWT) to generate the final mark. Afterward, embed the generated watermark into computed tomography (CT) scan images of the COVID-19 patient and general images through employing the RDWT and RSVD. Further, we use a hybrid of particle swarm optimization (PSO) and Firefly optimization techniques to determine the optimal scaling factor for embedding purposes. After that, the watermarked CT scan image is encrypted using an encryption technique based on a nonlinear-chaotic map, random permutation, and singular value decomposition (SVD). Extensive evaluations establish the benefit of our proposed algorithm over the traditional schemes. The optimal robustness is more effective than the five traditional schemes at lower computational efficiency.

In recent years, smart healthcare systems have gained popularity due to the ease of sharing e-patient records over the open network. The issue of maintaining the security of these records has attracted many researchers. Thus, robust and dual watermarking based on redundant discrete wavelet transform (RDWT), Hessenberg Decomposition (HD), and randomized singular value decomposition (RSVD) are put forward for CT scan images of COVID-19 patients. To ensure a high level of authentication, multiple watermarks in form of Electronic Patient Record (EPR) text and medical image are embedded in the cover. The EPR is encoded via turbo code to reduce /eliminate the channel noise if any. Further, both imperceptibility and robustness are achieved by a fuzzy inference system, and the marked image is encrypted using a lightweight encryption technique. Moreover, the extracted watermark is denoised using the concept of deep neural network (DNN) to improve its robustness. Experiment results and performance analyses verify the proposed dual watermarking scheme.

With the development of the industrial internet technology, the medical data exchange in IoT systems has become more prosperous. Specially, more and more medical images produced by industrial and intelligent devices are outsourced to the cloud for convenient use. However, IoT systems deployment poses several medical data security challenges. To address this issue, in this article, a robust medical data hiding scheme based on secure hybrid optimization for industrial scenario image is presented. Specifically, the marked image is obtained through non-subsampled shearlet transform-multiresolution singular value decomposition. In order to generate the dual marks, we employ the Fisher–Yates permutation to produce the scrambled system watermark address for embedding into the mark image. Afterward, the generated mark image is embedded in the chosen coefficients of the cover in an invisible way. After the watermarking, a hybrid optimization-based encryption scheme is utilized to secure the marked image. Extensive experiments demonstrate the invisibility, security, and robustness of our scheme. Further, the superiority of the scheme is elaborated through making the comparison with the other similar algorithms. The solution not only performs the robust exchange of medical data but also protects the privacy of patients.

Over the recent years, volume of medical images and related digital records, called electronic medical records, generated, shared, and stored by different intelligent devices, sensors, and Internet of medical things networks, to name a few, has drastically increased. Such records are shared by cloud providers for storage and further processing. However, an increasingly serious concern is the illegal copying, modification, and forgery of medical records. This article presents a visible and imperceptible medical data hiding technique, namely ViMDH, which can prevent to intellectual property theft of medical records. The carrier image is visibly marked with logo mark, which is suitable for owner identification and avoid illegal duplication, and then an imperceptible data hiding based on nonsubsampled shearlet transform (NSST), redundant discrete wavelet transform (RDWT), and multiresolution singular value decomposition is introduced. Finally, key-based encryption scheme designed by RDWT-RSVD ensure the security of the watermarking system. Under the experimental evaluation, our ViMDH is not only visible and imperceptible, but also has a satisfactory advantage in robustness and security compared with the traditional watermarking schemes.

Fusing the single modality medical images to obtain a distinct multimodality image is needed to ensure a better clinical experience. However, the distribution of these fused images brings the issues of ownership and authentication and has attracted many researchers. Presently, large volumes of medical data are stored on cloud platforms. However, outsourcing medical data to this popular platform may introduce security issues. Following this, we introduce a secure data hiding in fused medical image for smart healthcare, since it is also suitable for applications in the cloud. To achieve this, we first create a fused medical image as a cover by nonsubsampled contourlet transform (NSCT). The method, which is based on NSCT, QR, and Schur decomposition, allows concealing the image and electronic patient records (EPR) mark into the fused image. Importantly, EPR watermark includes a hash value of cover is created first and then embedded into the cover via magic cube-based procedure. Finally, the marked image is encrypted using deoxyribonucleic acid (DNA), chaotic maps, and a hash function-based encryption scheme. The introduced watermarking scheme has been evaluated using 25 pairs of medical images and several embedding/extracting parameters. Apart from being satisfactorily imperceptible, the proposed work is also robust and secure against well-known signal processing attacks and promising results are obtained when compared with similar techniques. It indicates a considerable improvement in robustness of 66.7% and 99.7% over existing discrete wavelet transform (DWT)–singular value decomposition (SVD)-based watermarking schemes.

Nowadays, there is an increasing tendency to fuse multi-modal medical image due to the high diagnostic accuracy and assessment. However, the distribution of these images and more generally, health records may bring serious copy-violation issues in healthcare applications. Further, a massive amount of electronic health records is uploaded on the cloud due to the low cost and high usability. However, outsourcing these records to the cloud may greatly increase security and privacy issues. The present article develops medical image fusion-based watermarking scheme in dual tree complex wavelet transform (DTCWT)-singular value decomposition (SVD) domain. Here, computed tomography (CT) and magnetic resonance imaging (MRI) images are partitioned into a sequence of high frequencies and low frequency by non-subsampled shearlet transform (NSST) to obtain a fused image as a carrier. To enhance the authenticity, spatial and transform domain-based embedding is conducted to conceal multi-marks into carrier image. Finally, improved encryption scheme is employed to obtain a secure marked carrier image. Objective and subjective evaluations indicate a strong robustness effect (43.64%) of the proposed algorithm compared to other methods with remarkable invisibility, payload, and security. To the best of our knowledge, studies of image fusion-based dual watermarking and encryption have not yet been developed in combination with spatial and transform domain schemes for copy protection and ownership of health records, since it is also suitable for applications in the cloud.

Deep learning-based methods have been popular in multimedia analysis tasks, including classification, detection, segmentation, and so on. In addition to conventional applications, this model can be widely used for cover communication, i.e., information hiding. This article presents a review of deep learning-based covert communication scheme for protecting digital contents, devices, and models. In particular, we discuss the background knowledge, current applications, and constraints of existing deep learning-based information hiding schemes, identify recent challenges, and highlight possible research directions. Further, major role of deep learning in the area of information hiding are highlighted. Then, the contribution of surveyed scheme is also summarized and compared in the context of estimation of design objectives, approaches, evaluation metric, and weaknesses. We believe that this survey can pave the way to new research in this crucial field of information hiding in deep-learning environment.

With the remarkable development of internet technologies, the popularity of smart healthcare has regularly come to the fore. Smart healthcare uses advanced technologies to transform the traditional medical system in an all-round way, making healthcare more efficient, more convenient, and more personalized. Unfortunately, medical data security is a serious issue in the smart healthcare systems. It becomes a fundamental challenge that requires the development of efficient innovative strategies towards fulfilling the healthcare needs and supporting secure healthcare transfer and delivery. This article provides a comprehensive survey on state-of-the-art techniques for health data security and their new trends for solving challenges in real-world applications. We survey the various notable cryptography, biometrics, watermarking, and blockchain-based security techniques for healthcare applications. A comparative analysis is also performed to identify the contribution of reviewed techniques in terms of their objective, methodology, type of medical data, important features, and limitations. At the end, we discuss the open issues and research directions to explore the promising areas for future research.