AI-Generated Image and Video Synthesis

Deep Learning Models, Applications, and Ethical Implications in Visual Media Creation

Arvind Mewada, Mohd. Aquib Ansari, Shahnawaz Ahmad, Nagendra Singh

• Publisher: Wiley-IEEE Press
• Year: 2026
• Print ISBN:
  9781394403110
• Edition: 1
• eBook ISBN:
  9781394403134
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

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.

Table of contents

• Cover
• Table of Contents
• IEEE Press
• Title Page
• Copyright
• Contributors
• Foreword
• About the Editors
• Preface
• Acknowledgements
• Acronyms
• Introduction
• Chapter 1: Introduction to AI-generated Image and Video Synthesis
• Chapter 2: LoomNet: An Assam Handloom Fabric Dataset
• Chapter 3: Sensors-to-synthesis: Edge AI and IoT for Generative Visual Systems
• Chapter 4: Detecting AI-generated Images in the Social Media Era: A Deep Learning Approach with GenReal Dataset
• Chapter 5: Raindrop Removal in Images and Videos Using Generative AI: A Survey
• Chapter 6: A Transfer Learning Baseline and a GAN-augmentation Perspective for MRI-based Alzheimer’s Disease Detection
• Chapter 7: Advanced Foundations and Future Trends in Generative AI for Visual Media
• Chapter 8: High-resolution GAN Augmentation with Ensemble CNN Models for Accurate Skin Cancer Detection
• Chapter 9: Content-aware Convolutional VAE for Anime Face Synthesis
• Chapter 10: GEN-HAR: Generative Diffusion Learning for Human Activity Recognition
• Chapter 11: Hybrid Neural Networks for Robust Deepfake Detection: Integrating CNN-RNN and Residual Attention Architectures
• Index
• End User License Agreement