SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
Browse
2 results
Search Results
Item Restricted Explainable AI Approach for detecting Generative AI Imagery(Aston University, 2024-09-29) Alghamdi, Sara; Barns, ChloeThe rapid advancement of Artificial Intelligence (AI) and machine learning, particularly deep learning models such as Convolutional Neural Networks (CNNs), has revolutionized image classification across diverse fields, including healthcare, autonomous vehicles, and digital forensics. However, the proliferation of AI-generated images, commonly referred to as deepfakes, has introduced significant ethical, societal, and security challenges. Deepfakes leverage AI to create highly realistic yet synthetic media, complicating the ability to differentiate between authentic and manipulated content. This has heightened the need for robust tools capable of accurately detecting and classifying such media to combat the risks of misinformation, fraud, and erosion of public trust. Traditional models, while effective in classification, often lack transparency in their decision-making processes, limiting stakeholder trust. To address this limitation, this study explores the integration of Explainable AI (XAI) techniques, such as SHAP (SHapley Additive exPlanations), with CNNs to enhance interpretability and trust in model predictions. By employing CNNs for high-accuracy classification and XAI methods for feature-level explanations, the research aims to contribute to digital forensics and content moderation, offering both technical reliability and transparency. This study highlights the critical need for trustworthy AI systems in the fight against manipulated media, providing a framework that balances efficacy, transparency, and ethical considerations.48 0Item Restricted The Dance Of Order And Chaos: Tracking Keywords Evolution in a Community Over Time(Saudi Digital Library, 2023) Alhazmi, Arwa; Andreas, Gutmann; Ismini, Psychoula; Murdoch, StevenOnline platforms face a persistent challenge in managing prohibited content. As they act to curtail undesired content by blocking search results for specific search terms (keywords) used by malicious actors, they inadvertently impact their associated benign content. Simultaneously, malicious actors cleverly adapt by introducing intentional language variations to those terms. This risks blocking further innocent content and creates openings for undesired content to remain undetected. Therefore, while bad actors’ use of specialized language offers opportunities for content management, it also raises the need for systems adept at detecting the specific terms used across different timeframes, to thwart their efforts efficiently. In this research, we utilize a publicly available time-series dataset of online posts (news articles) to track keyword evolution over time. We posit that methods adept at capturing these shifts can enhance analysis and consequently, the precision of search terms blocking. Our methods leveraged diverse NLP techniques. Firstly, to track change in keywords, news articles were categorized using BERTopic, keywords were extracted for each article using KeyBERT, and afterward, keywords were sampled and carefully represented utilizing tf-idf for different periods. Subsequently, periods were clustered using hierarchical agglomerative clustering to identify patterns and trends. Secondly, our method for tracking contextual change in keywords consisted of identifying keywords, identifying different topics keywords’ representative articles belong to, and setting criteria for defining prominent and shifting topics. Our analysis has yielded promising results, demonstrating that the clustering approach we have adopted for tracking change is adept for handling time-series keywords. Its strength lies in discerning evolving patterns and temporal shifts in keywords and providing insights into ideal time frames for such monitoring. Notably, we were able to identify recurring or seasonal trends, shortterm trends, extended trends, and distinctive keywords isolated within a single month. Moreover, our method and criteria for tracking and analyzing keywords’ usage evolution between through different contexts have proven effective, as evidenced by identifying a contextual shift in 16% of the top 1,000 keywords in our dataset.17 0