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In today’s digital landscape, maintaining the integrity and ownership of digital content is crucial across various fields, including the critical domain of medical applications. However, existing watermarking techniques face significant challenges, such as vulnerability to attacks and limitations in capacity and robustness. To address these challenges, this work presents a novel hybrid reversible watermarking approach utilizing the Lucas number series (LNS), normalization, and squint ternary least significant bit (STLSB). Additionally, the method incorporates a lightweight encryption technique based on homomorphic binomial coefficients (LHBC) to encrypt watermark images. The encrypted image is then embedded into the cover image at the STLSB position, specifically three bits from the left-most significant bit, resulting in a watermarked image. The Present issues in watermarking medical data include ensuring the watermark’s robustness against attacks while maintaining data integrity and confidentiality, and balancing the need for traceability with the risk of compromising sensitive patient information. Experimental results show that the proposed method significantly improves embedding capacity by approximately 10% compared to existing techniques, achieving a PSNR of 52.0 dB, NCC of 1, SSIM of 0.9967, and consistently low BER, demonstrating enhanced robustness against various attacks, including JPEG compression, sharpening, resizing, and rotation. These findings highlight the method’s effectiveness for secure medical image watermarking applications, ensuring integrity and confidentiality, and extending to practical uses in digital asset management, copyright protection, and authentication systems across diverse industries.

Smart Healthcare has brought advantages to both healthcare professionals and individuals living in remote areas. However, deliberately or inadvertently altering medical records can result in an inaccurate diagnosis. As a result of telemedicine, patients can easily communicate with their healthcare providers, and the Internet of Medical Things (IoMT) devices enable remote consultations, allowing for timely diagnosis and treatment. Medical data can be protected by watermarking, a combination of steganography, and fingerprinting. This paper proposes a hash-based approach using the Discrete Cosine Integer Wavelet Transform (DCIWT) to provide secure watermarking for confidential medical data. To preserve critical Region of Interest (RoI) integrity, we use SHA-256 cryptographic hash algorithm. The hashed Region of Interest (hRoI) is embedded in the Region of Non-Interest (RoNI) by DCIWT. To enhance the segmentation process, we employ adaptive thresholding, which dynamically adjusts the threshold based on local image characteristics, ensuring accurate segmentation of RoI and RoNI across varying image qualities. The hidden data is significantly affected if the watermarked image is altered. As a result of comparing the original RoI with the extracted RoI, we determine if an image has been altered. Tampered regions are be recovered by extracting the RoI and using it for diagnosis. The proposed digital watermarking technique maintains RoI integrity using message digests and histograms. Through extensive experiment results, we demonstrate that the perceptual properties of the images are intact in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).

Abstract Globally, data maintenance and its security are vital tasks due to the rapid development of advanced technologies. Users now utilise Cloud computing technique and security techniques to preserve their data securely from intruders and hackers. Even yet, because of the technology's rapid advancement and inherent insecurity, attackers are conducting assaults on the cloud data. Hence, a homomorphic encryption technique was proposed based on Matrix Transformations with shifts, rotations, and transpositions of Binary converted ASCII values of each character in the plain text. For both encryption and decryption, symmetric cryptography employs the same secret key. The “avalanche effect” is a desirable feature of symmetric encryption in which two distinct keys generate separate cipher texts for the same message. As there are different conditions for the key, it helps to achieve this effect in this technique. The suggested algorithm's cryptanalysis reveals that it is more powerful than the existing encryption methods and resistant to a variety of attacks. So that an attacker cannot easily predict a plaintext through a statistical analysis.

The use of advanced technologies has increased drastically to maintain any sensitive records related to education, health, or finance. It helps to protect the data from unauthorized access by attackers. However, all the existing advanced technologies face some issues because of their uncertainties. These technologies have some lapses to provide privacy, attack-free, transparency, reliability, and flexibility. These characteristics are essential while managing any sensitive data like educational certificates or medical certificates. Hence, we designed an Industry 5.0 based blockchain application to manage medical certificates using Remix Ethereum blockchain in this paper. This application also employs a distributed application (DApp) that uses a test RPC-based Ethereum blockchain and user expert system as a knowledge agent. The main strength of this work is the maintenance of existing certificates over a blockchain with the creation of new certificates that use logistic Map encryption cipher on existing medical certificates while uploading into the blockchain. This application helps to quickly analyze the birth, death, and sick rate as per certain features like location and year.

Augmented Reality (AR) is an innovation that empowers us in coordinating computerized data into the client's real-world space. It offers an advanced and progressive methodology for medicines, providing medication training. AR aids in surgery planning, and patient therapy discloses complex medical circumstances to patients and their family members. With accelerated upgrades in innovation, the ever-increasing number of medical records get accessible, which contain a lot of sensitive medical data, similar to medical substances and relations between them. To exploit the clinical texts in these data, it is important to separate significant data from these texts. Drugs, along with some kind of the fundamental clinical components, additionally should be perceived. Drug name recognition (DNR) tries to recognize drugs specified in unstructured clinical texts and order them into predefined classifications, which is utilized to deliver a connected 3D model inside the present reality client space. This work shows the utilization of AR to give an active and visual representation of data about medicines and their applications. The proposed method is a mobile application that uses a native camera and optical character recognition algorithm (OCR) to extract the text on the medicines. The extracted text is over and above processed using natural language processing (NLP) tools which are then used to identify the generic name and category of the drug using the dedicated DNR database. The database used for the system is scraped using various resources of medical studies and is named a medi-drug database from a development standpoint. 3D model prepared particularly for the drug is then presented in AR using ArCore. The results obtained are encouraging. The proposed method can detect the text with an average time of 0.005 s and can produce the visual representation of the output with an average time of 1.5 s.

Fog environment adoption rate is increasing day by day in the industry. Unauthorized accessing of data occurs due to the preservation of Identity and information of the users either at the endpoints or at the middleware. This paper proposes a methodology to protect and preserve the Identity during data transmission of the users. It uses fog computing for storage against security issues in the cloud and database environment. Cloud and database architectures failed to protect the data and Identity of users but the Fog computing based Identity management as a service (IDaaS) system can handle it with Security Assertion Mark-up Language (SAML) protocol and Pentatope based Elliptic Curve Crypto cipher. A detailed comparative study of the proposed and existing techniques is investigated by considering multi-authentication dialogue, security services, service providers, Identity, and access management.

In today's society, voting is crucial to choosing the representatives of the people. The current voting process is filled with a vast array of disputes and manipulations. The leader must be selected in a precise manner without any malpractices. In addition, the people and authorities are not happy with the election results and label them unpredictable. We offer a better solution to the current problems, such as tampering, non-residents voting outside of the polling place, quick results analysis, quick counting, and reduced use of staff and funds during the electoral franchise process. In this offer, blockchain technology is used to create the distributed application (dApp) framework that will be used for the proposed e-voting system. Additionally, it offers unique characteristics such as immutability, transparency, privacy, and reception freedom that reduce crimes involving the processing of sensitive data in the electoral process. Ganache, MetaMask, and specified dagger hashing algorithm are used to develop the dApp. A key strength of this paper is the statistical analysis of transactions on the blockchain. Moreover, it also provides security to voters' identity and leads to immediate acceptable counting results with more accuracy. [ABSTRACT FROM AUTHOR]

With the growing demand for smart, secure, and intelligent solutions, Industry 4.0 has emerged as the future of various applications. One of the primary sectors that are becoming more vulnerable to security assaults like ransomware is the healthcare sector. Researchers have proposed various mechanisms in smart and secure health care systems with this vision in mind. Existing systems are vulnerable to security attacks on medical data. It is required to build a real-time diagnosis device using a cyber-physical system with blockchain technology in a considerable manner. The proposed work's main purpose is to build secure, real-time preservation and tamper-proof control of medical data. In this work, the Bayesian grey filter-based convolution neural network (BGF-CNN) approach is used to enhance accuracy and reduce time complexity and overhead. Additionally, PSO and GWO optimization techniques are used to improve network performance. As an outcome of the proposed work, the privacy preservation of medical data is improved with a high accuracy rate by a blockchain-based cyber-physical system using a deep neural network (BGF Blockchain). To summarize, the proposed system helps in the privacy preservation of medical data along with a reduction in communication overhead using the Bayesian Grey Filter–CNN. [ABSTRACT FROM AUTHOR]

The detection of water bodies from satellite images plays a vital role in research development. It has a wide range of applications such as the prediction of natural disasters and detecting drought and flood conditions. There were few existing applications that focused on detecting water bodies that are becoming extinct in nature. The dataset to train this deep learning model is taken from Kaggle. It has two classes, namely water bodies and masks. There is a total of 2841 sentinel-2 satellite images with corresponding 2841 masks. Additionally, the present work focuses on using UNet, Tensorflow to detect the water bodies. It uses a Nadam optimizer to reduce the losses. It also finds best-optimized parameters for the activation function, a number of nodes in each layer. This proposed model achieves integrity by embedding a security feature Elliptic Curve Digital Signature Algorithm (ECDSA). It generates a digital signature for the predicted area of water bodies which helps to secure the key and the detected water bodies while transmitting in a channel. Thus, the proposed model ensures the performance accuracy of 94% which can also work the same for edge detection, detection in blurred and low-resolution images. The model is highly robust. [ABSTRACT FROM AUTHOR]

Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof. [ABSTRACT FROM AUTHOR]

Particularly in the last decade, Internet usage has been growing rapidly. However, as the Internet becomes a part of the day to day activities, cybercrime is also on the rise. Cybercrime will cost nearly $6 trillion per annum by 2021 as per the cybersecurity ventures report in 2020. For illegal activities, cybercriminals utilize any network computing devices as a primary means of communication with a victims' devices, so attackers get profit in terms of finance, publicity and others by exploiting the vulnerabilities over the system. Cybercrimes are steadily increasing daily. Evaluating cybercrime attacks and providing protective measures by manual methods using existing technical approaches and also investigations has often failed to control cybercrime attacks. Existing literature in the area of cybercrime offenses suffers from a lack of a computation methods to predict cybercrime, especially on unstructured data. Therefore, this study proposes a flexible computational tool using machine learning techniques to analyze cybercrimes rate at a state wise in a country that helps to classify cybercrimes. Security analytics with the association of data analytic approaches help us for analyzing and classifying offenses from India-based integrated data that may be either structured or unstructured. The main strength of this work is testing analysis reports, which classify the offenses accurately with 99 percent accuracy. [ABSTRACT FROM AUTHOR]