Vol. 3 No. 4 (2025)

Open Access

Article

Article ID: 3893

Verifying artificial intelligence-generated images: Socio-technical approaches to authenticity

by Michael Mncedisi Willie

Computing and Artificial Intelligence, Vol.3, No.4, 2025;

The rapid proliferation of artificial intelligence (AI) has transformed visual media, enabled highly realistic AI-generated images, and raised ethical, social, and security concerns. Generative artificial intelligence (Generative AI) architectures, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, allow content creation that is increasingly indistinguishable from human-made visuals, facilitating creativity, education, and communication. However, these capabilities also introduce risks of manipulation, identity fraud, misinformation, and deepfake attacks across social, political, corporate, academic, and humanitarian domains. This study investigates AI image verification as a socio-technical response to synthetic visuals, focusing on social media, artistic, and forensic contexts. It employed a qualitative design combining thematic literature review and case study analysis. Thematic analysis identified patterns in verification approaches, including pixel-level analysis, metadata forensics, machine learning classifiers, watermarking, and blockchain-enabled methods. Case studies explored real-world applications, highlighting perceptual biases, strategic use of synthetic content, and governance and digital literacy challenges. Findings reveal that human perception alone is insufficient for reliably discerning authenticity, with individuals frequently misclassifying AI-generated images as real. Integrating machine learning, metadata analysis, and blockchain verification, hybrid technical approaches significantly improve detection accuracy. Socio-technical factors, including platform policies, ethical norms, organisational governance, and user literacy, shape the effectiveness of verification methods. The study presents a conceptual framework linking technological, organisational, and societal dimensions, emphasising the need for coordinated strategies that combine algorithmic innovation, regulatory oversight, and public engagement. Practical implications include deploying hybrid verification systems, strengthening governance and ethical standards, enhancing digital literacy, and fostering cross-disciplinary collaboration to safeguard trust, authenticity, and integrity in digital media.

Open Access

Article

Article ID: 4397

Leveraging extensive feature modeling for facial emotion recognition

by Milica Tufegdzic, Nevena Tufegdzic, Marija Mojsilovic

Computing and Artificial Intelligence, Vol.3, No.4, 2025;

Facial emotion recognition (FER) is an important area of affective computing with applications in human–computer interaction, healthcare, education, and intelligent systems. Although recent FER research is largely dominated by deep learning and transformer-based approaches, handcrafted feature modeling remains attractive due to its interpretability and lower computational requirements. This study proposes an Action Unit–based machine learning (AU-ML) framework for recognizing basic emotions from lateral facial expressions using the Karolinska Directed Emotional Faces (KDEF) dataset. Facial Action Units (AUs) were extracted, and manual feature selection was performed to retain only AU intensity and presence information relevant to emotion recognition. This process significantly reduced the original feature vector, improving computational efficiency while preserving classification performance. To compensate for the reduced dataset size after extracting lateral images, data augmentation techniques, including horizontal flipping, shifting, scaling, rotation, and brightness and contrast adjustments, were applied prior to AU extraction. Several machine learning algorithms were evaluated, including K-Nearest Neighbors, Support Vector Classifier, Decision Tree, Naïve Bayes, Random Forest, AdaBoost, Bagging, Voting, and Stacking Classifiers, CatBoost, and Extreme Gradient Boosting. The results demonstrated that ensemble methods generally outperformed simpler classifiers, with CatBoost achieving the best classification performance. The findings indicate that extensive feature modeling remains a reliable approach to emotion recognition, and that AU-based representations provide an interpretable and computationally efficient alternative to deep learning approaches.

Open Access

Article

Article ID: 4261

Next-generation VANET routing: An AI-based time-evolving graph framework

by Ali Hamlili

Computing and Artificial Intelligence, Vol.3, No.4, 2025;

Vehicular Ad Hoc Networks (VANETs) exhibit highly dynamic topologies due to rapid vehicle mobility and frequent fluctuations in wireless connectivity. Consequently, efficient routing remains a significant challenge. Proactive routing protocols rely on periodic route updates. This creates a trade-off between routing accuracy and control overhead. Higher update frequencies improve accuracy and timeliness. However, they also generate substantial control overhead. Conversely, lower update frequencies reduce overhead at the cost of outdated topology information and increased routing errors. Achieving an optimal balance between these competing objectives is particularly difficult in non-stationary network environments, a well-documented challenge in VANETs. This paper introduces a unified framework combining stochastic modeling, graph analysis, and machine-learning optimization. Vehicle positions follow a Poisson process, while link dynamics are modeled with a two-state Markov chain. We derive closed‑form expressions that quantify network connectivity, expected link lifetime, and overall routing stability in dynamic vehicular environments. An artificial intelligence (AI)-driven adaptive controller is integrated into the proposed framework to dynamically adjust routing parameters in response to variations in VANET topology and non-stationary network conditions. Simulation results demonstrate a 35–50% reduction in routing overhead while preserving high packet delivery performance.

Open Access

Review

Article ID: 4296

From bench to field: A systematic review of computer vision for tomato detection in precision agriculture (2018–2025)

by Philippe Lyonel Mbouembe Touko, Guoxu Liu

Computing and Artificial Intelligence, Vol.3, No.4, 2025;

Accurate tomato detection enables robotic harvesting, crop yield estimation, and tomato quality control, among other agricultural tasks. Despite remarkable advances in computer vision, particularly YOLO models, a significant gap persists between laboratory research and field deployment. This PRISMA-guided review of 110 publications (2018-2025) analyzes the disparity between lab-tested model performance and reliable real-world performance in this context. To quantify current capabilities, we construct a taxonomy based on the sensing platform, task, and environment. Across the reviewed literature, the mean average precision has increased from 78.3% for YOLOv3 to 94.7% for YOLOv11, while a controlled benchmarking study on an identical dataset (LaboroTomato) reveals smaller differences. However, tomato detection performance significantly drops in deployment, with a mean cross-domain performance loss of 8.24% due to occlusion, illumination changes, and weather conditions. Our reproducibility audit shows that most research lacks protocols for model development and that 12% releases make their public code available. Finally, 73% of high-accuracy models have requirements above the popular edge-device sizes commonly used in agricultural robotics. To bridge this implementation gap, we outline: 1) reporting guidelines to promote reproducibility, 2) decision frameworks to translate pragmatic agricultural considerations into concrete technical specifications, and 3) open research directions centered on reliability, cross-domain validation, and real-world deployment. This survey will support practitioners in agriculture, robotics, and machine learning design, deployable computer vision systems for tomatoes and other crops.