Micro-task crowdsourcing marketplaces like Figure Eight (F8) connect a large pool of workers to employers through a single online platform, by aggregating multiple crowdsourcing platforms (channels) under a unique system. This paper investigates the F8 channels’ demographic distribution and reward schemes by analysing more than 53k crowdsourcing tasks over four years, collecting survey data and scraping marketplace metadata. We reveal an heterogeneous per-channel demographic distribution, and an opaque channel commission scheme, that varies over time and is not communicated to the employer when launching a task: workers often will receive a smaller payment than expected by the employer. In addition, the impact of channel commission schemes on the relationship between requesters and crowdworkers is explored. These observations uncover important issues on ethics, reliability and transparency of crowdsourced experiment when using this kind of marketplaces, especially for academic research.

Identifying hidden payloads in images has become increasingly critical as steganography continues to challenge traditional security measures. This paper introduces a deep learning framework for both the detection (binary classification) and fine-grained classification (multi-class) of steganographic payloads embedded using Least Significant Bit (LSB) techniques. The proposed system distinguishes between benign images and stego images containing five different payload types: HTML, JavaScript, PowerShell, URLs, and Ethereum-related data. To achieve this, we systematically evaluate various architectures, including a custom Convolutional Neural Network (CNN), hybrid CNN-GRU and CNN-LSTM models, and a Vision Transformer (ViT) at different input resolutions using 5-fold cross-validation. Our experiments reveal a critical finding: image resizing significantly degrades detection performance, as subtle LSB artifacts are often corrupted. While our custom CNN model achieved the highest mean cross-validation accuracy (0.9702), the hybrid CNN-GRU model demonstrated superior generalization on the held-out test set and external dataset, achieving a multi-class accuracy of 0.98 on the testset and 0.97 on the external. This result highlights the advantage of combining the CNN’s spatial feature extraction with the GRU’s ability to model sequential dependencies for robust payload identification on unseen data.

Along with the coronavirus pandemic (COVID-19), some in the medical publication industry have observed an “infodemic”, which is more pandemic than the virus. Given the lack of sufficient pandemic preparedness measures in many countries, people started posting millions of posts on social media without questioning their veracity or accuracy, particularly within Arabic-speaking communities. This study investigates an unsupervised model for detecting fake news in Arabic to fight the infodemic. While there has been much research on fake news detection (FND) in English, this subject in Arabic has yet to be investigated enough in the literature. We examine the use of distribution-based clustering techniques for Arabic FND and show their performance compared to each other. Moreover, we conduct a comprehensive linguistic analysis, identifying significant differences in textual features between real and fake posts, which can improve fake news detection. Our research shows the potential of online learning techniques to enhance model performance, leading to high accuracy, reaching up to 92%. By addressing the unique challenges posed by Arabic-language posts, our research offers practical implications for developing effective strategies for reducing infodemics and their social consequences and for strategic planning to control the current and future infodemics.

Advances in computer vision and deep learning are reshaping tourism by enabling automated landmark recognition and enhancing visitor experience. This study evaluates EfficientNetB0 for recognizing 16 Saudi landmarks spanning cultural, historical, and modern sites, and integrates predictions with a bilingual (Arabic/English) metadata repository containing names, locations, historical context, and visiting recommendations. A curated dataset of 480 images (balanced via offline augmentation) was used, and the models were trained using a two-phase transfer learning procedure. Under 5-fold stratified cross-validation, EfficientNetB0 achieved a mean accuracy of 0.994 and an F1-score of 0.992, outperforming ResNet50 and MobileNetV3Small. On an external test set of unseen images, the model reached 92.5% top-1 accuracy, with errors concentrated among architecturally similar sites (e.g., Diriyah vs. Ibrahim Palace). A Gradio-based web interface provided near real-time recognition (177 ms) and enriched predictions with bilingual cultural metadata, achieving high usability (SUS 80–100). These findings demonstrate not only technical robustness but also practical contributions to smart tourism, aligning with Saudi Vision 2030 in cultural preservation, digital transformation, and sustainable tourism development.