Momina Shaheen – författare

Exploring the field of generative artificial intelligence (GenAI) and its use in the processing and security of multimedia content, this co-authored book addresses the critical needs and emerging challenges in the rapidly evolving intersection of artificial intelligence, multimedia content, and cybersecurity. The capabilities of sophisticated GenAI models in generating, improving, and manipulating multimedia information, including images, videos, and audio are thoroughly explored. Coverage also extends to technical innovations including advanced neural network topologies, novel training methods, and methods for boosting GenAI-generated content quality and authenticity.The book's focus on security and privacy concerns and the weaknesses brought on by GenAI includes data privacy challenges, the development of unlicensed material, and the exploitation of AI for nefarious intentions. Furthermore, the ethical implications and legal obstacles involved in guaranteeing the secure and accountable implementation of generative AI in multimedia applications are examined and potential future directions identified.Generative AI for Multimedia Content Processing, Security and Privacy: Fundamentals, advances and applications will be a useful reference for multimedia researchers and engineers working with GenAI, particularly those interested in security and privacy challenges. Cybersecurity researchers and engineers working on digital content security and synthetic media prevention, AI researchers and engineering and technology practitioners developing generative AI technologies, will all find relevant information in this volume. The book will also be of interest to regulatory and compliance experts and policy makers working in the field.

Federated Learning for the Metaverse: Applications in Virtual Environments provides readers with insights into how federated learning, a decentralized machine learning paradigm, can be strategically applied to address critical aspects of the metaverse. The book covers a wide range of topics, including privacy-preserving personalization, security, collaboration, adaptive learning environments, real-time communication, decentralized governance, language understanding, immersive learning experiences, avatar customization, and dynamic scene rendering.Explores practical applications of federated learning within the metaverse, examining real-world scenarios and showcasing successful implementationsOffers diverse perspectives from experts in the fields of federated learning, virtual reality, augmented reality, and immersive technologiesProvides practical guidance on implementing federated learning techniques within metaverse applications, accompanied by code snippets and case studiesAddresses the ethical considerations and implications of utilizing federated learning in the metaverse, including privacy concerns and data governanceDiscusses emerging trends at the intersection of federated learning and the metaverse

Traditional surveillance systems struggle to process large volumes of visual data, identify specific objects or behaviors, and adapt to dynamic environments. Computational intelligence, which encompasses techniques like artificial intelligence (AI), machine learning (ML), and computer vision, offers powerful tools to address these challenges by enabling automated analysis, pattern recognition, and decision-making based on visual data. Computational Intelligence in Surveillance Systems Using Image Processing addresses the unique challenges and ethical considerations of applying AI and ML, offering a nuanced understanding of the regulatory landscape. It provides insights into the responsible development and deployment of technologies to unlock the transformative potential of computational intelligence to revolutionize surveillance systems and advance the capabilities of security and monitoring across various sectors.Discusses emerging trends, potential challenges, and areas for future research, providing a roadmap for scholars looking to contribute to the evolving field of image processingExplains how AI and ML algorithms can be applied to analyze and interpret visual data captured by surveillance camerasConsiders the challenges and considerations associated with deploying computational intelligence in surveillance, including privacy concerns, ethical considerations, and technical limitationsExplores specific use cases and applications where computational intelligence can enhance surveillance capabilities, such as object detection, activity recognition, anomaly detection, and predictive analytics