Bader Fahad Alkhamees’s research while affiliated with King Saud University and other places

With expeditious advancements in AI-driven facial manipulation techniques, particularly deepfake technology, there is growing concern over its potential misuse. Deepfakes pose a significant threat to society, particularly by infringing on individuals’ privacy. Amid significant endeavors to fabricate systems for identifying deepfake fabrications, existing methodologies often face hurdles in adjusting to innovative forgery techniques and demonstrate increased vulnerability to image and video clarity variations, thereby hindering their broad applicability to images and videos produced by unfamiliar technologies. In this manuscript, we endorse resilient training tactics to amplify generalization capabilities. In adversarial training, models are trained using deliberately crafted samples to deceive classification systems, thereby significantly enhancing their generalization ability. In response to this challenge, we propose an innovative hybrid adversarial training framework integrating Virtual Adversarial Training (VAT) with Two-Generated Blurred Adversarial Training. This combined framework bolsters the model’s resilience in detecting deepfakes made using unfamiliar deep learning technologies. Through such adversarial training, models are prompted to acquire more versatile attributes. Through experimental studies, we demonstrate that our model achieves higher accuracy than existing models.

Forecasting speculative stock prices is essential for effective investment risk management and requires innovative algorithms. However, the speculative nature, volatility, and complex sequential dependencies within financial markets present inherent challenges that necessitate advanced techniques. In this regard, a novel framework, ACB-XDE (Attention-Customized BiLSTM-XGB Decision Ensemble), is proposed for predicting the daily closing price of speculative stock Bitcoin-USD (BTC-USD). The proposed ACB-XDE framework integrates the learning capabilities of a customized Bi-directional Long Short-Term Memory (BiLSTM) model with a novel attention mechanism and the XGBoost algorithm. The customized BiLSTM leverages its learning capabilities to capture complex sequential dependencies and speculative market trends. Meanwhile, the new attention mechanism dynamically assigns weights to influential features based on volatility patterns, thereby enhancing interpretability and optimizing effective cost measures and volatility forecasting. Moreover, XGBoost handles nonlinear relationships and contributes to the proposed ACB-XDE framework’s robustness. Furthermore, the error reciprocal method improves predictions by iteratively adjusting model weights based on the difference between theoretical expectations and actual errors in the individual attention-customized BiLSTM and XGBoost models. Finally, the predictions from both the XGBoost and attention-customized BiLSTM models are concatenated to create a varied prediction space, which is then fed into the ensemble regression framework to improve the generalization capabilities of the proposed ACB-XDE framework. Empirical validation of the proposed ACB-XDE framework involves its application to the volatile Bitcoin market, utilizing a dataset sourced from Yahoo Finance (Bitcoin-USD, 10/01/2014 to 01/08/2023). The proposed ACB-XDE framework outperforms state-of-the-art models with a MAPE of 0.37%, MAE of 84.40, and RMSE of 106.14. This represents improvements of approximately 27.45%, 53.32%, and 38.59% in MAPE, MAE, and RMSE respectively, over the best-performing attention-BiLSTM. The proposed ACB-XDE framework presents a technique for informed decision-making in dynamic financial landscapes and demonstrates effectiveness in handling the complexities of BTC-USD data.

Background Foreign object debris (FOD) is an unwanted substance that damages vehicular systems, most commonly the wheels of vehicles. In airport runways, these foreign objects can damage the wheels or internal systems of planes, potentially leading to flight crashes. Surveys indicate that FOD-related damage costs over $4 billion annually, affecting airlines, airport tenants, and passengers. Current FOD clearance involves high-cost radars and significant manpower, and existing radar and camera-based surveillance methods are expensive to install. Methods This work proposes a video-based deep learning methodology to address the high cost of radar-based FOD detection. The proposed system consists of two modules for FOD detection: object classification and object localization. The classification module categorizes FOD into specific types of foreign objects. In the object localization module, these classified objects are pinpointed in video frames. Results The proposed system was experimentally tested with a large video dataset and compared with existing methods. The results demonstrated improved accuracy and robustness, allowing the FOD clearance team to quickly detect and remove foreign objects, thereby enhancing the safety and efficiency of airport runway operations.

The alarming growth of misinformation on social media has become a global concern as it influences public opinions and compromises social, political, and public health development. The proliferation of deceptive information has resulted in widespread confusion, societal disturbances, and significant consequences for matters pertaining to health. Throughout the COVID-19 pandemic, there was a substantial surge in the dissemination of inaccurate or deceptive information via social media platforms, particularly X (formerly known as Twitter), resulting in the phenomenon commonly referred to as an “Infodemic”. This review paper examines a grand selection of 600 articles published in the past five years and focuses on conducting a thorough analysis of 87 studies that investigate the detection of fake news connected to COVID-19 on Twitter. In addition, this research explores the algorithmic techniques and methodologies used to investigate the individuals responsible for disseminating this type of fake news. A summary of common datasets, along with their fundamental qualities, for detecting fake news has been included as well. For the purpose of identifying fake news, the behavioral pattern of the misinformation spreaders, and their community analysis, we have performed an in-depth examination of the most recent literature that the researchers have worked with and recommended. Our key findings can be summarized in a few points: (a) around 80% of fake news detection-related papers have utilized Deep Neural Networks-based techniques for better performance achievement, although the proposed models suffer from overfitting, vanishing gradients, and higher prediction time problems, (b) around 60% of the disseminator related analysis papers focus on identifying dominant spreaders and their communities utilizing graph modeling although there is not much work done in this domain, and finally, (c) we conclude by pointing out a wide range of research gaps, for example, the need of a large and robust training dataset and deeper investigation of the communities, etc., and suggesting potential solution strategies. Moreover, to facilitate the utilization of a large training dataset for detecting fake news, we have created a large database by compiling the training datasets from 17 different research works. The objective of this study is to shed light on exactly how COVID-19-related tweets are beginning to diverge, along with the dissemination of misinformation. Our work uncovers notable discoveries, including the ongoing rapid growth of the disseminator population, the presence of professional spreaders within the disseminator community, and a substantial level of collaboration among the fake news spreaders.

The population growth is drastically surging in demand for food and water and uplifting consumption and waste resulting in overburden of society and the environment. Urgent actions are required to address these emerging global issues. Therefore, adopting a circular economy (CE) is essential to sustain the consumption rate and accommodate the ever-increasing demand. Moreover, the CE practices accelerate the progress on sustainable development. From this perspective, digital technologies are playing driving roles in the successful implementations of CE practices and achievements of the United Nations’ (UN) sustainable development goals (SDGs). Among various emerging digital technologies, artificial intelligence (AI) and digital twin ((DT) are the promising ones. This paper aims to understand and explore how both technologies facilitate the CE transitions and attain SDGs in the agriculture domain. To this end, we provide insights into the concepts of CE, AI, and DT with preliminary and current research status. This research evaluates the contributions of global organizations for CE transitions. We elaborate on the significant contributions of AI and DT in the transition towards CE and identify some challenges that hinder the adoption of these technologies. Besides expanding knowledge, concise multiple case studies are also presented as evidence to depict how companies in China are deploying these technologies to digitize various operations and create solutions for waste management, sustainable resource consumption, renewal energy, water conservation, etc. Findings reveal that these companies successfully attain many SDGs of 1, 2, 6, 7, 9, 11, 12, 13, 14, 15, and 17. This paper enormously contributes to the emerging research domain of integrating CE, AI, DT, and agriculture.

Once the shared secret key is established, three parties can use it for secure communication using symmetric-key encryption AES (128, 192, 256) algorithms or other cryptographic primitives. Although there are few third-party post-quantum authentication and key agreement protocols exist, but the recent studies in this article show that they are not anonymous or cannot provide forward secrecy. Most of the existing protocols enable adversaries to trace the source of messages. Many of third-party AKA schemes based on conventional public-key cryptosystems are vulnerable to quantum computers. Therefore, this paper contains a forward secure three-party post-quantum authenticated key establishment protocol for mobile devices. The proposed three-party key exchange protocol establishes an authenticated shared key that can be periodically refreshed to maintain forward secrecy. This protocol enables two parties to establish a shared session key even in the presence of quantum adversaries and enables them to communicate confidentially and securely over insecure networks. The protocol is anonymous as both the parties communicate using masked dynamic identities. A contrast study consisting of performance and security assessment is presented, which illustrates the suggested design is more applicable.

In the rapidly advancing domain of smart manufacturing, securing data integrity and preventing unauthorized access are critical challenges. This study introduces a novel approach that synergizes anomaly detection techniques with Zero-Knowledge Proofs (ZKPs) to fortify the security framework of smart manufacturing systems. Our methodology employs a combination of data preprocessing, including statistical imputation and data smoothing, alongside advanced anomaly detection using classification methods and neural networks, particularly focusing on deep learning architectures. The detected anomalies undergo verification through zk-SNARKs, a specialized ZKP scheme, ensuring a robust validation process without compromising data confidentiality. Our findings reveal a notable enhancement in the accuracy of anomaly detection, achieving detection rates of approximately 95% for temperature fluctuations and 90% for pressure irregularities, with a significant reduction in false positives. This performance is markedly superior to traditional methods and aligns closely with the highest efficacy rates reported in contemporary studies. Moreover, the utilization of ZKPs for anomaly verification demonstrated a 98% success rate, ensuring the secure and private verification of anomalies. The integration of anomaly detection with ZKPs presents a significant leap forward in addressing the security vulnerabilities inherent in smart manufacturing. This study not only showcases the effectiveness of our approach in enhancing data security and integrity but also sets a benchmark for future research in creating more resilient and trustworthy industrial operations.

Breast cancer is widespread worldwide and can be cured if diagnosed early. Mammography is an irreplaceable and critical technique in modern medicine, serving as a foundation for breast cancer detection. In medical imaging, the reliability of synthetic mammogram images is produced by Deep Convolutional Generative Adversarial Networks (DCGANs). The human validation for assessing the quality of synthetic images for examining and calculating the perceptual variations between synthetic images and real-world counterparts is a difficult task. Thus, this research focused on improving the quality and authenticity of synthetic mammogram images. For this, we explored and identified a new research gap because the radiologists consistently expressed much higher confidence levels in real mammogram images in their assessment process. This research highlights the key difference between synthetic and real mammograms by defining mean scores. The defined mean identifies a sizable gap, with real mammographic images receiving an average score of 0.73 and a synthetic score of 0.31. we performed a statistical analysis, which yielded a T-statistic of -6.35, a p-value less than 0.001, and a 95% confidence interval ranging from -0.50 to -0.28. These results have a wide range of ramifications. It emphasizes the urgent need for further generative model improvement, improving the legitimacy and caliber of synthetic mammogram images. Our research highlights how crucial it is to incorporate synthetic images into clinical practice with caution and thought. The Ethical considerations must encompass the potential consequences of relying on synthetic data in medical decision-making, alongside concerns related to diagnostic accuracy and patient safety. In future research, we work on improving generative models, leveraging more extensive and more diverse datasets. Comprehensive validation studies, encompassing a broader spectrum of radiologists and datasets, are pivotal to validate and generalize our findings. This study sets the stage for a deeper understanding of the validity of synthetic mammogram images.

Truck platooning uses networking technology and automated driving support systems to join multiple trucks in a group. When these vehicles interact for particular journey stages, such as on highways, they autonomously maintain a predefined, tight spacing among themselves. Platooning improves transportation by making better use of highways, delivering cargo faster, and minimizing congestion in traffic. Therefore, safety and platooning are two important at tributes of an intelligent truck system. This paper discusses a quantum-safe blockchain-empowered authentication mechanism for autonomous truck platooning. The proposed idea is to use blockchain to combine multiple nodes and ensure authenticity with the help of an aggregation technique. The proposed design ensures authenticity to the system due to hard assumptions, (1) Learning With Error (LWE), and (2) Short Integer Solution (SIS) on random module generated lattice. This paper uses operations over module lattices to be more efficient than general lattices. We can perform operations on module lattices with the help of fast algorithms for polynomial arithmetic.