Overview

Technical depth and ethical frameworks for AI visual media synthesis Generative AI models for visual media are transforming virtual reality and biomedical imaging while raising urgent questions about deepfakes and misinformation. AI-Generated Image and Video Synthesis addresses both dimensions. A team of researchers provide algorithmic foundations alongside detection strategies, authentication methods, and regulatory analysis. Coverage spans text-to-image generation, image-to-image translation, video synthesis, neural rendering, and 3D-aware generation. The book examines AI applications in CT, MRI synthetic data augmentation, and virtual staining for biomedical contexts. Case studies explore AI-assisted filmmaking, music videos, and style transfer. A dedicated chapter forecasts emerging trends including diffusion-transformer hybrids and autonomous generative agents. Readers will also find: Comparative analyses of generative models including GANs, diffusion models, and transformers with implementation guidance and code repositories for hands-on experimentation Deepfake detection strategies and digital content authentication techniques addressing misinformation, intellectual property rights, and emerging regulatory frameworks worldwide Industry case studies demonstrating real-world deployments in creative industries, surveillance systems, education, and cultural preservation applications Biomedical imaging applications covering synthetic data generation for CT and MRI, virtual staining techniques, and data augmentation strategies Practical toolkits supporting implementation and evaluation of AI synthesis techniques across professional and academic contexts Designed for AI researchers, computer vision engineers, and graduate students studying deep learning and image processing, this book connects theoretical principles with practical deployment. The combination of technical depth, application coverage, and ethical analysis makes it a comprehensive resource for professionals navigating AI-generated visual media.

Full Product Details

Author:   Arvind Mewada (Bennett University, India) ,  Mohd. Aquib Ansari (Bennett University, India) ,  Shahnawaz Ahmad (Bennett University, India) ,  Nagendra Singh (Trinity College of Engineering & Technology, India)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-IEEE Press
ISBN:  

9781394403110

ISBN 10:   1394403119
Pages:   192
Publication Date:   11 August 2026
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

Contributors Foreword Preface Acknowledgments Acronyms Introduction 1 Introduction to AI-Generated Image and Video Synthesis 1 1.1 Introduction 1.2 Foundations of AI-Generated Media 1.3 Image Synthesis Techniques 1.4 Video Synthesis and Manipulation 1.5 Applications for AI-Generated Media 1.6 Ethical and Societal Considerations 1.7 Future Directions and Challenges 1.8 Conclusion 2 LoomNet: An Assam Handloom Fabric Dataset 49 2.1 Introduction 2.2 Methodology 2.3 Discussion and Future Work 2.4 Conclusion 3 Sensors-to-Synthesis: Edge AI and IoT for Generative Visual Systems 73 3.1 Introduction 3.2 Background and Literature Review 3.3 Architectural Framework 3.4 Methodological Framework and Workflow 3.5 Sensors-to-Synthesis Workflow of Generative Visual Systems 3.6 Generative Models and Edge AI for Visual Synthesis 3.7 Applications of Edge-AI-Driven Generative Visual Systems 3.8 Challenges and Future Directions 3.9 Conclusion 4 Detecting AI-Generated Images in the Social Media Era: A Deep Learning Approach with GenReal Dataset 99 4.1 Introduction 4.2 Literature Review 4.3 Methodology 4.4 Results 4.5 Conclusion and Future Scope 5 Raindrop Removal in Images and Videos Using Generative AI: A Survey 123 5.1 Introduction 5.2 Background and Preliminaries 5.3 Generative AI Approaches for Raindrop Removal 5.4 Datasets and Evaluation Metrics 5.5 Applications 5.6 Challenges and Open Issues 5.7 Future Directions 5.8 Conclusion 6 A Transfer Learning Baseline and a GAN-Augmentation Perspective for MRI-Based Alzheimer's Disease Detection 153 6.1 Introduction 6.2 Related Work 6.3 Materials and Methods 6.4 Results 6.5 Discussion 6.6 Conclusion 7 Advanced Foundations and Future Trends in Generative AI for Visual Media 185 7.1 Context and Advanced Foundations 7.2 Technology Landscape and Mathematical Formulations for Visual Synthesis 7.3 Model Trajectories and Scaling Strategies for Visual Synthesis 7.4 Evaluation Protocols, Benchmarks, Robustness, and Alignment for Visual Media 7.5 Systems Efficiency, Economics, and Deployment for Visual Synthesis 7.6 Applications and Translational Pathways for Visual Media 7.7 Open Problems and Research Agenda for Visual Synthesis 7.8 Conclusion and Outlook for Visual Synthesis 8 High-Resolution GAN Augmentation with Ensemble CNN Models for Accurate Skin Cancer Detection 229 8.1 Introduction 8.2 Literature Review 8.3 Proposed Methodology 8.4 Experimental Setup 8.5 Results and Discussion 8.6 Conclusion and Future Scope 9 Content-Aware Convolutional VAE for Anime Face Synthesis255 9.1 Introduction 9.2 Related Work 9.3 Proposed Model: Content-Aware CNN-VAE 9.4 Experimental Setup 9.5 Result and Analysis 9.6 Conclusion 10 GEN-HAR: Generative Diffusion Learning for Human Activity Recognition 281 10.1 Introduction 10.2 Related Work 10.3 Proposed Method: GEN-HAR 10.4 Experimental Analysis 10.5 Conclusion 11 Hybrid Neural Networks for Robust Deepfake Detection: Integrating CNN-RNN and Residual Attention Architectures 305 11.1 Introduction 11.2 Related Work 11.3 Problem Statement 11.4 Proposed Work 11.5 Experiments and Results 11.6 Conclusion and Future Work Bibliography

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Author Information

Arvind Mewada, PhD, is an Assistant Professor in the School of Computer Science Engineering and Technology at Bennett University, India. His research spans natural language processing, machine learning, and deep learning, with publications in Multimedia Tools and Applications and The Journal of Supercomputing. Mohd. Aquib Ansari, PhD, is an Assistant Professor at Galgotias University, India. A UGC-NET qualified scholar and M.Tech. Gold Medalist, his research focuses on computer vision, image processing, and human-computer interaction, with advances in surveillance systems and gesture recognition. Shahnawaz Ahmad, PhD, is an Assistant Professor at Bennett University, India. His expertise includes cloud computing security and machine learning. He reviews for IEEE Access, Elsevier, Springer, and Wiley, and is the author of Cloud Computing: An Industrial Approach. Nagendra Singh, PhD, is Principal of Trinity College of Engineering and Technology in India. He has published over 42 international journal articles, 9 conference papers, 3 Indian patents, and 4 books, contributing actively to IEEE and Scopus-indexed publications.

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