AI-Generated Image and Video Synthesis

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.

• Contributors xiForeword xiiiAbout the Editors xvPreface xviiAcknowledgements xixAcronyms xxiIntroduction xxiii1 Introduction to AI-generated Image and Video Synthesis 1Arvind Mewada, Mohd. Aquib Ansari, Shahnawaz Ahmad, and Nagendra Singh1.1 Introduction 11.2 Foundations of AI-generated Media 21.3 Image Synthesis Techniques 51.4 Video Synthesis and Manipulation 91.5 Applications for AI-generated Media 121.6 Ethical and Societal Considerations 141.7 Future Directions and Challenges 171.8 Conclusion 192 LoomNet: An Assam Handloom Fabric Dataset 23Anindita Das and Aniruddha Deka2.1 Introduction 232.2 Methodology 252.3 Discussion and Future Work 302.4 Conclusion 313 Sensors-to-synthesis: Edge AI and IoT for Generative Visual Systems 35Swati Vishnoi, Ankur Sisodia, Mayank Deep Khare, and Ajay Kumar Yadav3.1 Introduction 353.2 Background and Literature Review 363.3 Architectural Framework 383.4 Methodological Framework and Workflow 393.5 Sensors-to-synthesis Workflow of Generative Visual Systems 403.6 Generative Models and Edge AI for Visual Synthesis 403.7 Applications of Edge-AI-driven Generative Visual Systems 423.8 Challenges and future directions 433.9 Conclusion 444 Detecting AI-generated Images in the Social Media Era: A Deep Learning Approach with GenReal Dataset 47Akhil Sibi, Deepika Pantola, and Madhuri Gupta4.1 Introduction 474.2 Literature Review 484.3 Methodology 494.4 Results 514.5 Conclusion and Future Scope 545 Raindrop Removal in Images and Videos Using Generative AI: A Survey 59Mohd. Aquib Ansari, Vijay Dhote, Sonulal, Shahnawaz Ahmad, and Jiyaul Mustafa5.1 Introduction 595.2 Background and Preliminaries 605.3 Generative AI Approaches for Raindrop Removal 625.4 Datasets and Evaluation Metrics 645.5 Applications 665.6 Challenges and Open Issues 685.7 Future Directions 695.8 Conclusion 706 A Transfer Learning Baseline and a GAN-augmentation Perspective for MRI-based Alzheimer's Disease Detection 73Subiya Zaidi, Arvind Mewada, and Kapil Juneja6.1 Introduction 736.2 Related Work 756.3 Materials and Methods 776.4 Results 806.5 Discussion 816.6 Conclusion 847 Advanced Foundations and Future Trends in Generative AI for Visual Media 89Shwetang Dubey, Greetta Pinheiro, Reetu Singh, Mohd. Aquib Ansari, and Lalit Kumar7.1 Context and Advanced Foundations 897.2 Technology Landscape and Mathematical Formulations for Visual Synthesis 907.3 Model Trajectories and Scaling Strategies for Visual Synthesis 947.4 Evaluation Protocols, Benchmarks, Robustness, and Alignment for Visual Media 967.5 Systems Efficiency, Economics, and Deployment for Visual Synthesis 987.6 Applications and Translational Pathways for Visual Media 1017.7 Open Problems and Research Agenda for Visual Synthesis 1037.8 Conclusion and Outlook for Visual Synthesis 1058 High-resolution GAN Augmentation with Ensemble CNN Models for Accurate Skin Cancer Detection 109Amit D. Joshi, Ananya Patil, Niruppreet Kour, Parth Vora, and Tamizharasan P. S8.1 Introduction 1098.2 Literature Review 1108.3 Proposed Methodology 1128.4 Experimental Setup 1148.5 Results and Discussion 1158.6 Conclusion and Future Scope 1189 Content-aware Convolutional VAE for Anime Face Synthesis 123Lalit Kumar, Bhupchand Kumar, Shahnawaz Ahmad, Mohd. Aquib Ansari, and Greetta Pinheiro9.1 Introduction 1239.2 Related Work 1269.3 Proposed Model: Content-aware CNN-VAE 1289.4 Experimental Setup 1309.5 Results and Analysis 1319.6 Conclusion 13310 GEN-HAR: Generative Diffusion Learning for Human Activity Recognition 135Roshni Singh, Ataus Samad, Abhilasha Sharma, Vandana Bhatia, and Abdul Aleem10.1 Introduction 13510.2 Related Work 13610.3 Proposed Method: GEN-HAR 13710.4 Experimental Analysis 13810.5 Conclusion 14211 Hybrid Neural Networks for Robust Deepfake Detection: Integrating CNN-RNN and Residual Attention Architectures 147Rupesh Kumar Dewang, Arvind Mewada, Jamvant Omkar, Ayshwarya Jaiswal, and Nagendra Singh11.1 Introduction 14711.2 Related Work 14811.3 Problem Statement 14911.4 Proposed Work 15011.5 Experiments and Results 15311.6 Conclusion and Future Work 160References 161Index 163