Disinformation Security for Generative AI Applications

Concepts, Methodologies, and Use Cases
1. Auflage 2026
ISBN: 978-1-394-42236-4
Verlag: John Wiley & Sons Inc

Buch, Englisch, 512 Seiten

This book is an essential roadmap for AI researchers, security analysts, and policymakers, delivering actionable, cross-cutting frameworks to defend democratic institutions and digital ecosystems against the growing threat of AI-driven disinformation.

Generative artificial intelligence (AI) has ushered in a new era of content creation, enabling unprecedented capabilities in text, image, and video synthesis. However, these advancements come with significant risks, particularly the potential for AI to be exploited for disinformation. From deepfakes and synthetic text to AI-powered social engineering and adversarial attacks, malicious uses of generative AI pose serious threats to individuals, institutions, and democratic societies. Unlike traditional approaches that treat disinformation as a media or policy problem, this book frames it as a complex socio-technical security issue. It presents a layered perspective on disinformation threats, integrating advances in generative AI with detection strategies, policy frameworks, and ethical considerations. From adversarial machine learning and provenance tracking to explainable AI and decentralized content authentication, the book introduces cross-cutting methodologies to secure digital ecosystems against manipulation at scale.

At the heart of the book is the recognition that generative AI is both a tool and a threat. It can democratize creativity and intelligence, but without strong governance, it can just as easily erode truth, exploit biases, and amplify social discord. This duality is addressed through real-world use cases spanning sectors like journalism, healthcare, energy systems, and finance, where disinformation poses not just reputational risks but systemic vulnerabilities. The book highlights how technical innovation must be accompanied by transparency protocols, regulatory mechanisms, and ethical design to build societal resilience.

Readers will find the book: - Bridges theory and practice by combining foundational concepts with real-world case studies across healthcare, media, engineering, and energy;
- Covers the full threat landscape of generative AI, including deepfakes, synthetic text, adversarial attacks, and AI-powered social engineering;
- Presents actionable methodologies for detection, mitigation, watermarking, and provenance tracking of AI-generated content;
- Addresses ethical, regulatory, and governance frameworks, making it ideal for interdisciplinary audiences.

Audience

AI researchers, machine learning engineers, cybersecurity professionals, data scientists, academics, and policymakers working on generative models, adversarial robustness, AI-driven threats, synthetic media, and digital forensics.

Autoren/Hrsg.

Kumar, T. Ananth

Balakrishnan, S.

Nandhini, S. Aasha

Satheesh Kumar, M.

Balamurugan, S.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
• Technische Wissenschaften Technik Allgemein Technische Zuverlässigkeit, Sicherheitstechnik

Inhaltsverzeichnis

Series Preface xix
Preface xxi

Part 1: Foundations and Advances in Generative AI and Machine Learning 1

1 Recent Advances in Generative Modeling: A Unified Review of GANs, Transformers, Autoencoders, Diffusion Models, and Large Language Architectures 3
S. Tamil Selvi and P. Visalakshi

1.1 Introduction 4
1.2 Generative Models 6
1.3 GAI Task and Tools 19
1.4 Industrial Application of GIA 23
1.5 Challenges of GAI 26
1.6 The Future of GAI 27
1.7 Conclusion 29

2 Machine Learning for Fake Content Detection—Developing Classifiers and Anomaly Detection for AI-Generated Text, Images, and Videos 35
Naveenkumar M., Vidhya Saraswathy S. S. and Srinathi B.

2.1 Introduction 36
2.2 Related Work 39
2.3 Methodology 41
2.4 Feature Engineering 46
2.5 Machine Learning Techniques 52
2.6 Deep Learning Techniques: Ways Machines Learn Similar to Us Humans 58
2.7 Result and Discussion 62
2.8 Evaluation Metrics 66
2.9 Case Studies and Real-World Application 71
2.10 Challenges and Limitations 74
2.11 Conclusion and Future Scope 76

3 A Transformer-Based Hybrid Machine Learning Approach for Detecting AI-Generated Misinformation 81
R. Pari, Likhith Muthavarapu, Parvinder Kaur, Anurag Reddy Ekkati, P. Lakshmi Prasanna and P. Jeyabharathi

3.1 Introduction 82
3.2 Literature Review 84
3.3 Overview of AI-Generated Misinformation 89
3.4 Proposed Hybrid Machine Learning Approach 93
3.5 Experimental Setup 96
3.6 Results Analysis and Discussion 98
3.7 Conclusion and Future Enhancement 101

Part 2: Generative AI Applications in Healthcare and Biomedical Domains 105

4 Utilizing Generative AI and Foundation Models to Personalize Healthcare: Predictive Analytics and Customized Treatment Plans via Cascaded Deep Capsule Neural Networks 107
N. Naveenkumar, Seshukumar, J. Jude Moses Anto Devakanth, S. Balamurugan, P. Hanok and Deepthidotla

4.1 Introduction 108
4.2 Literature Survey 110
4.3 Proposed Methodology 114
4.4 Results and Discussions 120
4.5 Conclusion 128

5 Generative AI-Driven Patient Twins: Transforming Precision and Predictive Healthcare 133
Rajeswari R. and Kiruba Kumari

5.1 Introduction 134
5.2 Fundamentals of Generative AI 135
5.3 Foundations of DTs in Medicine 137
5.4 AI-Generated Patient Twins: A Conceptual Framework 141
5.5 The Role of Generative AI in Patient Twin Development 143
5.6 Applications of DT in Clinical Practice 145
5.7 Seven Integration into Precision and Predictive Medicine 147
5.8 Challenges in Implementing Digital Identical Twin in Healthcare 150
5.9 Future Directions: Toward Real-Time Adaptive Healthcare 153
5.10 Conclusion 154

6 Role of AI in Accurate and Disinformation-Resilient Diagnosis of Ulcerative Colitis 157
Latha D., Lakshmi D., Mohana R. and Nithiya A.

6.1 Introduction 158
6.2 Related Works 159
6.3 Methodology 161
6.4 Result and Discussion 172
6.5 Conclusion 177

7 A Generative AI Framework for Predicting Shear Strength Properties of Rock Materials Using ISSA-XGBoost 183
Tamilselvi. P.

7.1 Introduction 184
7.2 Understanding Generative AI 184
7.3 An Introduction to Rock Minerals 185
7.4 Integration of Generative AI in Geological Survey and Mineral Targeting 189
7.5 Gen-AI Algorithm to Discover the Rock Structure 193
7.6 Rock Structure Datasets 196
7.7 Rock Mechanics Applications 202
7.8 Conclusion 204
7.9 Future Work 204

Part 3: AI and Disinformation — Security, Detection, and Mitigation 209

8 Sign Language Recognition in the Age of AI with Disinformation Risks and Security Solutions 211
T. Grace Shalini, Sayed Sayeed Ahmad, Mrinalini Vaish, Neelansh Bhargava, Krishikaa Mathi Bharathi S. S. and Karthick Manoj. R.

8.1 Introduction 212
8.2 Literature Survey 214
8.3 Research Gap 217xii Contents
8.4 Proposed Methodology 219
8.5 Comparative Study and Tool Justification 226
8.6 Advantages Over Existing Systems 227
8.7 Results 229
8.8 Future Enhancements 232
8.9 Conclusion 234

9 EmoFusionNet: A Cutting-Edge Framework for Emotion Propagation with Attention-Enhanced Hypergraphs and Generative Temporal Forecasting 239
G. Akiladevi, M. Arun and J. Palanimeera

9.1 Introduction 240
9.2 Temporal Graph Neural Networks 248
9.3 Literature Review 249
9.4 Methodology 251
9.5 Conclusion 258

10 Advancing Trustworthy Content Verification through Explainable AI for Disinformation Mitigation 263
Barakkath Nisha U., Yasir Abdullah R., Anantraj I., Mary Posonia A. and Ram Shivany K.

10.1 Introduction 264Contents xiii
10.2 Theoretical Foundations 265
10.3 Techniques for Explainable AI 266
10.4 Architectures for Trustworthy Verification 267
10.5 Evaluating Trust and Explainability 270
10.6 Experimental Results and Analysis 272
10.7 Use Cases and Real-World Scenarios 278
10.8 Cross-Domain Trust Transferability 281
10.9 Human-AI Interaction and Trust Calibration 283
10.10 Policy, Regulation, and Ethical Mandates 286
10.11 Building Future-Ready Verification Frameworks 289
10.12 Challenges and Research Directions 291

11 Detecting Deepfakes in Generative Media with an Explainable CNN-Transformer Approach 299
R. Karthick Manoj, M. Arul Pugazhendhi, K. Suresh Kumar and Sunday Adeola Ajagbe

11.1 Introduction 300
11.2 Methodology 308
11.3 Results and Visualization 314
11.4 Conclusion 319

12 Analysis of Sentiment in Tamil Comments Using BERT and Explainable AI 323
N. Priyadharshini and N. Subbulakshmi

12.1 Introduction 324
12.2 Literature Survey 328
12.3 Datasets Availability 331
12.4 Dataset Used 331
12.5 Preprocessing 332
12.6 Constructing a Tamil Sentiment Lexicon 335
12.7 Handling Code-Mixed Tamil-English Text 336
12.8 Methodologies 336
12.9 Results and Evaluation 343
12.10 Cross-Lingual Transfer and Challenges 346
12.11 Case Studies 348
12.12 Future Works 349
12.13 Conclusion 349

13 Securing Generative AI Against Disinformation in Low-Stability Renewable Power Networks 353
R. Rajasree, D. Lakshmi, K. Stalin and R. Karthick Manoj

13.1 Introduction 354
13.2 Literature Survey 359
13.3 Proposed Methodology 367
13.4 Results and Discussion 370
13.5 Conclusion 374

Part 4: AI in Sustainable Development, Agriculture, and Smart Systems 379

14 Securing Agricultural IoT Systems Against Disinformation for Sustainable Rural Development 381
S. Murugesan, S. Ramalingam, P. Usharani and P. Kanimozhi

14.1 Introduction 382
14.2 Literature Review 387
14.3 Proposed Methodology 393
14.4 Results and Discussions 400
14.5 Conclusion 403

15 Resilient Embodied AI for Smart Farming: Tackling Misinformation in Bioacoustic Pest Detection 407
T. Grace Shalini, Sayed Sayeed Ahmad, Nayantra Ramakrishnan, Bhavesh P. and Karthick Manoj R.

15.1 Introduction 408
15.2 Literature Survey 410
15.3 Research Gaps 415
15.4 Proposed Methodology 417
15.5 Comparative Study and Tool Justification 427
15.6 Advantages Over Existing Systems 431
15.7 Future Enhancements 439
15.8 Conclusion 440

16 CNN-GA: Pose Recognition Using Region-Based Convolutional Neural Networks and Genetic Algorithm 445
J. Palanimeera, K. Ponmozhi and G. Akiladevi

16.1 Introduction 446
16.2 Related Works 457
16.3 Methodology 466
16.4 Experimental Results 468
16.5 Discussion 470
16.6 Conclusion 471

References 472
Index 475

Über Autor(innen)

T. Ananth Kumar, PhD is an Associate Professor Department of Computer Science and Engineering, IFET College of Engineering, Tamil Nadu, India. He has authored one book, several book chapters, and various papers in national and international conferences and journals.

S. Balakrishnan, PhD is a Professor and Head of the Department of Computer Science and Engineering, Vinayaka Mission’s Research Foundation University, Paiyanoor, Chennai, India. He has published more than 39 books, ten book chapters, and more than 150 publications in highly cited journals and conferences.

S. Aasha Nandhini, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India. She has published two books, six book chapters, and twelve publications in reputed international journals and conferences, and holds a granted patent.

M. Satheesh Kumar, PhD is an Associate Principal of Cybersecurity at LTI Mindtree with more than 17 years of?experience in the cybersecurity industry. He has published five journal articles, four book chapters, and one patent. His research interests include malware analysis, network security, and cyber forensics.

S. Balamurugan, PhD is the Director of Research at iRCS, an Indian Technological Research and Consulting Firm in Coimbatore, Tamil Nadu. He has published 75 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision making for leading companies and startups.