A drastic change in communication is happening with digitization. Technological advancements will escalate its pace further. The human health care systems have improved with technology, remodeling the traditional way of treatments. There has been a peak increase in the rate of telehealth and e-health care services during the coronavirus disease 2019 (COVID-19) pandemic. These implications make reversible data hiding (RDH) a hot topic in research, especially for medical image transmission. Recovering the transmitted medical image (MI) at the receiver side is challenging, as an incorrect MI can lead to the wrong diagnosis. Hence, in this paper, we propose a MSB prediction error-based RDH scheme in an encrypted image with high embedding capacity, which recovers the original image with a peak signal-to-noise ratio (PSNR) of ? dB and structural similarity index (SSIM) value of 1. We scan the MI from the first pixel on the top left corner using the snake scan approach in dual modes: i) performing a rightward direction scan and ii) performing a downward direction scan to identify the best optimal embedding rate for an image. Banking upon the prediction error strategy, multiple MSBs are utilized for embedding the encrypted PHR data. The experimental studies on test images project a high embedding rate with more than 3 bpp for 16-bit high-quality DICOM images and more than 1 bpp for most natural images. The outcomes are much more promising compared to other similar state-of-the-art RDH methods.

This book reviews research works in recent trends in blockchain, AI, and Digital Twin based IoT data analytics approaches for providing the privacy and security solutions for Fog-enabled IoT networks. Due to the large number of deployments of IoT devices, an IoT is the main source of data and a very high volume of sensing data is generated by IoT systems such as smart cities and smart grid applications. To provide a fast and efficient data analytics solution for Fog-enabled IoT systems is a fundamental research issue. For the deployment of the Fog-enabled-IoT system in different applications such as healthcare systems, smart cities and smart grid systems, security, and privacy of big IoT data and IoT networks are key issues. The current centralized IoT architecture is heavily restricted with various challenges such as single points of failure, data privacy, security, robustness, etc. This book emphasizes and facilitates a greater understanding of various security and privacy approaches using the advances in Digital Twin and Blockchain for data analysis using machine/deep learning, federated learning, edge computing and the countermeasures to overcome these vulnerabilities.

Heart attack is a leading cause of death around the world. Every year millions of people suffer because of heart attacks. Early detection of heart attack symptoms is crucial for the betterment of patient health, assisting in lowering the mortality rates. In recent years, machine learning (ML) has dominated as a promising tool for healthcare applications, including the early detection of heart attacks. The goal of this work is to explore machine learning methods, such as Decision Tree(DT) Algorithm, Logistic Regression(LR), and Support Vector Machine (SVM) on the data set to find an algorithm that obtains good results. The Kaggle data set is used for the experimental study. We train the input data after certain pre-processing tasks for training the models, and accuracy is calculated to justify the best machine learning model for heart attack detection. The logistic regression (LR) obtained good results (accuracy of 87%) among the three algorithms.

Life expectancy has improved with new-age technologies and advancements in the healthcare sector. Though artificial intelligence (AI) and the Internet of Things (IoT) are revolutionising smart healthcare systems, security of the healthcare data is always a concern. Reversible data hiding (RDH) is widely explored in the healthcare domain for secure data transmission and in areas like cloud computing, satellite image transmission, etc. Medical image transmission plays an important role in the smart health sector. In the case of medical images, a minute error in the reconstructed medical image can mislead the doctor, posing a threat to the patient’s health. Many RDH schemes have been proposed, but very few address from the view of medical images, and that too on high-quality DICOM images. The proposed AdaBPE RDH scheme is a solution for secure transmission of the patient’s health report (PHR) and other sensitive information with medical specialists. The scheme put forward a technique that maintains a good trade-off between the smooth pixels for maximum embedding in a block and a lossless recovery. Here, the cover medium employed to hide the patient’s sensitive information is an encrypted 16-bit DICOM image. The scheme processes the cover image as disjoint blocks of equal size, embedding the information adaptively within the encrypted blocks pertaining to the nature of the actual pixel values in the block through MSB prediction error methodology. The outcomes are evaluated on both the 16-bit DICOM images and 8-bit natural images, and the scheme is well poised with RDH goal of BER = 0, PSNR = ?, and SSIM = 1, achieving an average embedding of 5.7067 bpp on high-quality medical images and 1.6769 bpp on natural images. The experimental results prove advantageous and are better than other similar state-of-the-art schemes. © 2024 The Author(s). CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology.

Maintaining road infrastructure is essential to effective transportation systems and public safety. This research provides a new method for pothole depth estimation and automatic road crack detection using computer vision techniques. Our method utilizes convolutional neural networks (CNNs) for classifying road images into “With Crack” or “Without Crack” categories with high accuracy. Additionally, we employ image processing algorithms to detect and highlight cracks, providing insights into their lengths and percentages. Furthermore, we introduce a monocular depth estimation model to assess pothole depths, aiding in prioritizing road repair efforts. Experimental results demonstrate the effectiveness of our approach in accurately identifying road defects and estimating their severity. This research contributes to the advancement of intelligent infrastructure management systems, enabling proactive maintenance and ensuring safer roads for communities.

Reversible data hiding (RDH) is an active area of research in the field of information security. In RDH, a secret can be embedded inside a cover medium. Unlike other data-hiding schemes, RDH becomes important in applications that demand recovery of the cover without any deformation, along with recovery of the hidden secret. In this paper, a new RDH scheme is proposed for performing reversible data hiding in encrypted images using a Fibonacci transform with an ensemble learning method. In the proposed scheme, the data hider encrypts the original image and performs further data hiding. During data hiding, the encrypted image is partitioned into non-overlapping blocks, with each block considered one-by-one. The selected block undergoes a series of Fibonacci transforms during data hiding. The number of Fibonacci transforms required on a selected block is determined by the integer value that the data hider wants to embed. On the receiver side, message extraction and image restoration are performed with the help of the ensemble learning method. The receiver will try to perform all possible Fibonacci transforms and decrypt the blocks. The recovered block is identified with the help of trained machine-learning models. The novelty of the scheme lies in (1) retaining the encrypted pixel intensities unaltered while hiding the data. Almost every RDH scheme described in the literature alters the encrypted pixel intensities to embed the data, which represents a security concern for the encryption algorithm; (2) Introducing an efficient means of recovery through an ensemble model framework. The majority of votes from the different trained models guarantee the correct recovery of the cover image. The proposed scheme enables reduction in the bit error rate during message extraction and contributes to ensuring the suitability of the scheme in areas such as medical image transmission and cloud computing. The results obtained from experiments undertaken show that the proposed RDH scheme was able to attain an improved payload capacity of (Formula presented.) bits per pixel, outperforming many related RDH schemes with complete reversibility.

Reversible data hiding (RDH), a promising data-hiding technique, is widely examined in domains such as medical image transmission, satellite image transmission, crime investigation, cloud computing, etc. None of the existing RDH schemes addresses a solution from a real-time aspect. A good compromise between the information embedding rate and computational time makes the scheme suitable for real-time applications. As a solution, we propose a novel RDH scheme that recovers the original image by retaining its quality and extracting the hidden data. Here, the cover image gets encrypted using a stream cipher and is partitioned into non-overlapping blocks. Secret information is inserted into the encrypted blocks of the cover image via a controlled local pixel-swapping approach to achieve a comparatively good payload. The new scheme (Formula presented.) allows the data hider to hide two bits in every encrypted block. The existing reversible data-hiding schemes modify the encrypted image pixels leading to a compromise in image security. However, the proposed work complements the support of encrypted image security by maintaining the same entropy of the encrypted image in spite of hiding the data. Experimental results illustrate the competency of the proposed work accounting for various parameters, including embedding rate and computational time.

Reversible data hiding is an area explored widely in recent days due to its scope of applications in secure message transmission by embedding it in images. The existing RDH schemes in the encrypted image are lagging in terms of embedding rate. In this paper, we propose a new RDH scheme in encrypted images that will ensure the embedding rate without compromising the bit error rate or image recovery. In the proposed scheme, the encrypted image pixels will be classified into black-and-white pixels based on a checkerboard pattern. During the data hiding phase, the sender will select 8 unique random integer sequences S, that form the data keys and whose values are within the range 0 to 255. Data hiding is performed by performing bit-XOR with the white pixels in an image block (size B × B pixels) using one integer sequence from the 8 keys. Each key is correlated to a 3-bit sequence from the secret message. The receiver must have the data keys and the decryption key to extract the hidden message and recover the original image. A smoothness measure between adjacent pixels is defined and used for data extraction and image recovery. The experimental results show that the proposed scheme performs well on the standard image data set (USC-SIPI).

The research in the domain of reversible data hiding (RDH) is recently explored in all aspects due to its applications in cloud computing, forensics, and medical image communication. In this manuscript, we introduce a RDH scheme in encrypted images which can provide a high embedding rate without compromising the bit error rate during the message extraction and image recovery. The proposed scheme follows a block-wise data hiding process. If the processed block size is A×A pixels, and if A=2n+1, then the data hider can hide any number from the set {0, 1, …, (A+2n?1) } in that block. We have introduced an Arnold transform-based data hiding process in which each block will be undergoing a series of scrambling processes based on the bit sequence that we need to embed in the selected block. The message extraction and image restoration are carried out at the receiver side using a trained convolutional neural network (CNN) model.

In recent years, reversible data hiding (RDH) in encrypted images got much attention due to its wide applications in the areas such as cloud computing, military image transmission, medical image transmission, etc. This paper introduces a new solution for reversible data hiding in encrypted images. One of the main challenges while designing a reversible data hiding scheme in an encrypted image is the embedding rate and bit error rate during image recovery. The scheme proposed in this manuscript ensures a good embedding rate and the lossless recovery of the original image. The key idea behind the proposed technique is that the encrypted image will be partitioned into non-overlapping blocks, and the pixels in each block will be categorized into white pixels and black pixels based on a predefined pattern. The black pixels will be mapped into a new pixel value based on the two bits from the secret message that is to be embedded into the selected image block. For mapping purposes, we generate four different random permutations of all the possible gray-scale values (0 to 255). At the receiver side, corresponding to each block in the image we have to generate four different weighted mesh graphs. The image recovery and data extraction are carried out by analyzing the total edge weight of these mesh graphs. The results obtained from the experimental study are much better while comparing with a few of the well-known recently introduced reversible data hiding schemes in encrypted images.

Technological advancements and digitalization have made the life of humankind simple but at the same time imposing many challenges. As information started bursting across the internet, information management and security became major concerns. Recently, researchers have been focusing on a hot topic called reversible data hiding (RDH). RHD secures the data by covering it within another medium. It allows the recovery of the medium and hidden information on the receiver side without any loss. This work discloses a high capacity RDH scheme in the encrypted image with a Fibonacci transform image scrambling algorithm for data hiding and a convolutional neural network (CNN) based recovery. It follows a block-wise embedding process, embedding (n + 1) bits within a block of size 2n while n > 1. The proposed scheme is tested on the USC-SIPI image data set from the University of Southern California and has resulted in an improved embedding rate compared to the existing Arnold transform-based RDH and many other well-acknowledged RDH schemes.

Reversible data hiding (RDH) in encrypted images is an emerging research domain in the field of data security. Since the embedding rate from the existing RDH schemes in encrypted images is low, those schemes are not well suited for various real-life applications such as medical image transmission and cloud computing. To resolve this issue, in this manuscript we introduce an RDH scheme capable of providing high embedding without compromising the other efficiency parameters such as bit error rate and image recoverability. The proposed scheme uses a block-wise data hiding process in which a block of size B×B will be considered from the encrypted image at a time, and that will be scrambled through a sequence of Arnold transform. The bit sequence that the data hider wants to hide in a block will decide the number of Arnold transforming operations on the block. The same process will be continued for all the blocks in the image to get a final encrypted image with a hidden message. At the receiver side, the extraction of the hidden message and the image recovery is carried out with a trained support vector machine (SVM) model. The SVM model is capable to predict a given image block into any one of the two classes: encrypted block or natural block. The experimental study of the proposed scheme is carried out in the USC-SIPI image dataset and the results show that the new scheme surpasses the recent well-known RDH schemes.