A novel multipurpose watermarking scheme capable of protecting and authenticating images with tamper detection and localisation abilities

Technologies that fall under the umbrella of Industry 4.0 can be classified into one of its four significant components: cyber-physical systems, the internet of things (IoT), on-demand availability of computer system resources, and cognitive computing. The success of this industrial revolution lies in how well these components can communicate with each other, and work together in finding the most optimised solution for an assigned task. It is achieved by sharing data collected from a network of sensors. This data is communicated via images, videos, and a variety of other signals, attracting unwanted attention of hackers. The protection of such data is therefore pivotal, as is maintaining its integrity. To this end, this paper proposes a novel image watermarking scheme with potential applications in Industry 4.0. The strategy presented is multipurpose; one such purpose is authenticating the transmitted image, another is curtailing the illegal distribution of the image by providing copyright protection. To this end, two new watermarking methods are introduced, one of which is for embedding the robust watermark, and the other is related to the fragile watermark. The robust watermark's embedding is achieved in the frequency domain, wherein the frequency coefficients are selected using a novel mean-based coefficient selection procedure. Subsequently, the selected coefficients are manipulated in equal proportion to embed the robust watermark. The fragile watermark's embedding is achieved in the spatial domain, wherein self-generated fragile watermark(s) is embedded by directly altering the pixel bits of the host image. The effective combination of two domains results in a hybrid scheme and attains the vital balance between the watermarking requirements of imperceptibility, security and capacity. Moreover, in the case of tampering, the proposed scheme not only authenticates and provides copyright protection to images but can also detect tampering and localise the tampered regions. An extensive evaluation of the proposed scheme on typical images has proven its superiority over existing state-of-the-art methods

Study on high Performance and Effective Watermarking Scheme using Hybrid Transform (DCT-DWT)

Nowadays healthcare infrastructure depends on Hospital Information Systems (HIS), Radiology Information Systems (RIS),Picture archiving and Communication Systems (PACS) as these provide new ways to store, access and distribute medical data . It eliminates the security risk. Conversely, these developments have introduced new risks for unsuitable deployment of medical information flowing in open networks, provided the effortlessness with which digital content can be manipulated. It is renowned that the integrity and confidentiality of medical data is a serious topic for ethical and legal reasons. Medical images need to be kept intact in any condition and prior to any operation as well need to be checked for integrity and verification. Watermarking is a budding technology that is capable of assisting this aim. In recent times, frequency domain watermarking algorithms have gained immense importance due to their widespread use. Subsequently, the watermark embedding and extraction are performed in frequency domain using the presented scheme. The proposed watermarking scheme, the watermark extraction compared with the original image for calculating SSIM.The effectiveness of the proposed watermarking scheme is demonstrated with the aid of experimental results

Secure & trusted communication in emergency situations

In this paper we propose SETS, a protocol with main aim to provide secure and private communication during emergency situations. SETS achieves security of the exchanged information, attack resilience and user’s privacy. In addition, SETS can be easily adapted for mobile devices, since field experimental results show the effectiveness of the protocol on actual smart-phone platforms

An Enhanced Security Model for Protecting Data Transmission and Communication in Recent IoT Integrated Healthcare Industry Using Machine Learning Algorithm

Different kinds of security need to be applied to various application-centric IoT networks. Safety is one of the most important aspects to be considered regarding user, device, and data. The healthcare industry is a special IoT network fully connected with medical/healthcare IoT devices. The data generated from the IoT devices are transmitted or shared from one hospital to another through the Internet. Healthcare data has more private, medical, and insurance information that intruders can use on the Internet. The intruders misbehave with the patient or the general public registered in the healthcare industry. Some intruders blackmail the patient based on their private/personal information. Healthcare industries and their research team are trying to create a security framework to safeguard the data to avoid these malicious activities. This paper aims to secure and analyze healthcare IoT data using the Support Vector Machine algorithm. It learns the entire dataset, classifies it, and calls the encryption-decryption algorithms (RSA) to secure private data. The proposed SVM and the RSA algorithm are implemented in Python, and the results are verified. The performance of the proposed SVM-RSA is evaluated by comparing its results with the other algorithms

Secure Mutual Self-Authenticable Mechanism for Wearable Devices

YesDue to the limited communication range of wearable devices, there is the need for wearable devices to communicate amongst themselves, supporting devices and the internet or to the internet. Most wearable devices are not internet enabled and most often need an internet enabled broker device or intermediate device in order to reach the internet. For a secure end to end communication between these devices security measures like authentication must be put in place in other to prevent unauthorised access to information given the sensitivity of the information collected and transmitted. Therefore, there are other existing authentication solutions for wearable devices but these solutions actively involve from time to time the user of the device which is prone to a lot of challenges. As a solution to these challenges, this paper proposes a secure point-to-point Self-authentication mechanism that involves device to device interaction. This work exploits existing standards and framework like NFC, PPP, EAP etc. in other to achieve a device compatible secure authentication protocol amongst wearable device and supporting devices.

Verification approach for medical data in e-healthcare system based on biometric and watermarking

Medical information is crucial in the healthcare system, and its manipulation can lead to misdiagnosis. Medical images also contain personal information for patients; hence, information security and privacy protection are paramount when transferring medical images over the Internet. Biometric approach and watermarking techniques are used to achieve this purpose. The focus of this paper was on a biometric watermarking system with a frequency domain in which the sender's iris code is employed as a sender authentication key. The privacy of the patient's information is preserved by encrypting it and embedding the key in the cover medical image created by the Discrete Wavelet Transform. The algorithm has shown that the proposed system has met previous requirements

Maintaining privacy for a recommender system diagnosis using blockchain and deep learning.

The healthcare sector has been revolutionized by Blockchain and AI technologies. Artificial intelligence uses algorithms, recommender systems, decision-making abilities, and big data to display a patient's health records using blockchain. Healthcare professionals can make use of Blockchain to display a patient's medical records with a secured medical diagnostic process. Traditionally, data owners have been hesitant to share medical and personal information due to concerns about privacy and trustworthiness. Using Blockchain technology, this paper presents an innovative model for integrating healthcare data sharing into a recommender diagnostic computer system. Using the model, medical records can be secured, controlled, authenticated, and kept confidential. In this paper, researchers propose a framework for using the Ethereum Blockchain and x-rays as a mechanism for access control, establishing hierarchical identities, and using pre-processing and deep learning to diagnose COVID-19. Along with solving the challenges associated with centralized access control systems, this mechanism also ensures data transparency and traceability, which will allow for efficient diagnosis and secure data sharing

Oxalis: A Distributed, Extensible Ophthalmic Image Annotation System

Currently, ophthalmic photographers and clinicians write reports detailing the location and types of disease visible in a patient's photograph. When colleagues wish to review the patient's case file, they must match the report with the image. This is both inefficient and inaccurate. As a solution to these problems, we present Oxalis, a distributed, extensible image annotation architecture, implemented in the Java programming language. Oxalis enables a user to: 1) display a digital image, 2), annotate the image with diagnoses and pathologies using a freeform drawing tool, 3) group images for comparison, and 4) assign images and groups to schematic templates for clarity. Images and annotations, as well as other records used by the system, are stored in a central database where they can be accessed by multiple users simultaneously, regardless of physical locality. The design of Oxalis enables developers to modify existing system components or add new ones, such as display capabilities for a new image format, without editing or recompiling the entire system. System components can elect to be notified when data records are created, modified, or removed, and can access the most current system data at any point. While Oxalis was designed for ophthalmic images, it represents a generic architecture for image annotation applications