In the rapidly evolving digital landscape, the generation of fake visual, audio, and textual content poses a significant threat to society’s trust, political stability, and integrity of information. The generation process has been enhanced and simplified using Artificial Intelligence techniques, which have been termed deepfake . Although significant attention has been paid to visual and audio deepfakes, there is also a burgeoning need to consider text-based deepfakes. Due to advancements in natural language processing and large language models, the potential of manipulating textual content to reshape online discourse and misinformation has increased. This study comprehensively examines the multifaceted nature and impacts of deep-fake-generated media. This work explains the broad implications of deepfakes in social, political, economic, and technological domains. State-of-the-art detection methodologies for all types of deepfake are critically reviewed, highlighting the need for unified, real-time, adaptable, and generalised solutions. As the challenges posed by deepfakes intensify, this study underscores the importance of a holistic approach that intertwines technical solutions with public awareness and legislative action. By providing a comprehensive overview and establishing a framework for future exploration, this study seeks to assist researchers, policymakers, and practitioners in navigating the complexities of deepfake phenomena.

The Internet of Things (IoT) has had a tremendous impact on the evolution and adoption of information and communication technology. In the modern world, data are generated by individuals and collected automatically by physical objects that are fitted with electronics, sensors, and network connectivity. IoT sensor networks have become integral aspects of environmental monitoring systems. However, data collected from IoT sensor devices are usually incomplete due to various reasons such as sensor failures, drifts, network faults and various other operational issues. The presence of incomplete or missing values can substantially affect the calibration of on-field environmental sensors. The aim of this study is to identify efficient missing data imputation techniques that will ensure accurate calibration of sensors. To achieve this, we propose an efficient and robust imputation technique based on k-means clustering that is capable of selecting the best imputation technique for missing data imputation. We then evaluate the accuracy of our proposed technique against other techniques and test their effect on various calibration processes for data collected from on-field low-cost environmental sensors in urban air pollution monitoring stations. To test the efficiency of the imputation techniques, we simulated missing data rates at 10–40% and also considered missing values occurring over consecutive periods of time (1 day, 1 week and 1 month). Overall, our proposed BFMVI model recorded the best imputation accuracy (0.011758 RMSE for 10% missing data and 0.169418 RMSE at 40% missing data) compared to the other techniques (kNearest-Neighbour (kNN), Regression Imputation (RI), Expectation Maximization (EM) and MissForest techniques) when evaluated using different performance indicators. Moreover, the results show a trade-off between imputation accuracy and computational complexity with benchmark techniques showing a low computational complexity at the expense of accuracy when compared with our proposed technique.

Widespread adoption of smart IoT devices is accelerating research for new techniques to make IoT applications secure, scalable, energy-efficient, and capable of working in mission-critical use cases, which require an ability to function offline. In this context, the novel combination of distributed ledger technology (DLT) and distributed intelligence (DI) is seen as a practical route towards the decentralisation of IoT architectures. This paper surveys DI techniques in IoT and commences by briefly explaining the need for DI, by proposing a comprehensive taxonomy of DI in IoT. This taxonomy is then used to review existing techniques and to investigate current challenges that require careful attention and consideration. Based on the taxonomy, IoT DI techniques can be classified into five categories based on the factors that support distributed functionality and data acquisition: cloud-computing, mist-computing, distributed-ledger-technology, service-oriented-computing and hybrid. Existing techniques are compared and categorized mainly based on related challenges, and the level of intelligence supported. We evaluate more than thirty current research efforts in this area. We define many significant functionalities that should be supported by DI frameworks and solutions. Our work assists system architects and developers to select the correct low-level communication techniques in an integrated IoT-to-DLT-to-cloud system architecture. The benefits and shortcomings of different DI approaches are presented, which will inspire future work into automatic hybridization and adaptation of DI mechanisms. Finally, open research issues for distributed intelligence in IoT are discussed.

The past two decades have seen an increase in the deployment of photovoltaic installations as nations around the world try to play their part in dampening the impacts of global warming. The manufacturing of solar cells can be defined as a rigorous process starting with silicon extraction. The increase in demand has multiple implications for manual quality inspection. With automated inspection as the ultimate goal, researchers are actively experimenting with convolutional neural network architectures. This review presents an overview of the electroluminescence image-extraction process, conventional image-processing techniques deployed for solar cell defect detection, arising challenges, the present landscape shifting towards computer vision architectures, and emerging trends.

The introduction of 5G communication capabilities presents additional challenges for the development of products and services that can fully exploit the opportunities offered by high bandwidth, low latency networking. This is particularly relevant to an emerging interest in the Industrial Internet of Things (IIoT), which is a foundation stone of recent technological revolutions such as Digital Manufacturing. A crucial aspect of this is to securely authenticate complex transactions between IIoT devices, whilst marshalling adversarial requests for system authorisation, without the need for a centralised authentication mechanism which cannot scale to the size needed. In this article we combine Physically Unclonable Function (PUF) hardware (using Field Programmable Gate Arrays—FPGAs), together with a multi-layer approach to cloud computing from the National Institute of Standards and Technology (NIST). Through this, we demonstrate an approach to facilitate the development of improved multi-layer authentication mechanisms. We extend prior work to utilise hardware security primitives for adversarial trojan detection, which is inspired by a biological approach to parameter analysis. This approach is an effective demonstration of attack prevention, both from internal and external adversaries. The security is further hardened through observation of the device parameters of connected IIoT equipment. We demonstrate that the proposed architecture can service a significantly high load of device authentication requests using a multi-layer architecture in an arbitrarily acceptable time of less than 1 second.