Electronics and Communications Technology

Advanced Algorithms and Mathematical Models for Forensic Image Processing Editors: Jay Kumar Pandey, PhD Mritunjay Rai , PhD Ordering Info/Buy Book

In Production Pub Date: Forthcoming July 2027 Hardback Price: $200 US | £150 UK Hard ISBN: 9781779648853 E-Book ISBN: 978-1-77964-886-0 Pages: Est 398 pp w index Binding Type: hardbound / ebook Notes: 14 color and 27 b/w illustrations

This new volume, Advanced Algorithms and Mathematical Models for Forensic Image Processing, offers a rigorous and forward-looking exploration of the technologies transforming modern forensic investigations. As digital images and videos become central evidentiary artifacts in criminal justice, cybersecurity, surveillance, homeland security, and disaster analysis, the demand for accurate, explainable, and legally defensible forensic methodologies continues to grow. This volume responds to that demand by integrating advanced mathematical modeling, artificial intelligence, machine learning, and deep learning techniques with practical forensic image analysis applications.

Bridging theory with real-world implementation, the book examines image authentication, forgery and tampering detection, biometric identification, multispectral and hyperspectral imaging, denoising, enhancement, reconstruction, and explainable AI frameworks. Foundational mathematical tools, including probability theory, linear algebra, optimization, graph theory, and statistical inference, are explored alongside cutting-edge computational approaches designed to improve reliability, reproducibility, and interpretability in forensic workflows.

The contributors present detailed discussions of passive and active forensic methods, deepfake detection, machine learning–based anomaly detection, and AI-driven enhancement systems capable of recovering critical evidentiary details from degraded images. Real-world case studies, algorithmic explanations, and application-oriented insights provide readers with both conceptual clarity and practical guidance. Ethical considerations, transparency, and judicial admissibility are also emphasized, ensuring that emerging forensic technologies are examined within broader legal and societal contexts.

Key features include:
• Provides comprehensive coverage of forensic image authentication, enhancement, and tampering detection
• Explores mathematical foundations, including probability, optimization, matrix operations, and statistical inference
• Examines AI-, machine learning–, and deep learning–driven forensic methodologies
• Describes explainable AI frameworks for transparent and legally defensible forensic analysis
• Highlights practical applications through case studies, algorithmic workflows, and emerging forensic technologies

Designed for researchers, academicians, postgraduate students, investigators, and industry professionals, this book serves as a comprehensive technical resource at the intersection of computer science, mathematics, and forensic science.

CONTENTS:
Preface

1. Introduction to Forensic Image Processing: Importance, Challenges, and the Role of AI and Mathematical Models
Shalini Singh and Pushkar Singh Rawat

2. AI-Driven Neutrosophic Multi-Criteria Decision-Making Models for Forensic Image Processing: Enhancing Authentication and Tamper Detection
Ajoy Kanti Das, Nandini Gupta, Suman Das, and Takaaki Fujita

3. Pixels, Proofs, and Probabilities: Mathematical Pillars of Forensic Image Analysis
A. M. Khan, Lalita Mistry, and Hemant Purohit

4. Enhanced Denoising Techniques Using Deep Learning Algorithms for Forensic Images
S. Anthony Mariya Kumari, Viji Vinod, and Sidharth Muralidharan

5. Image Enhancement for Forensic Applications
Mahnoor Mirjat, Madeha Memon, Sanam Narejo, and Mehak Memon

6. AI-Driven Forensic Image Enhancement
Ritwik Raj Saxena

7. Machine Learning Techniques for Forensic Image Analysis: Supervised vs. Unsupervised Learning in Forensic Applications
Petros Chavula, Mulala Jimaima, James Shabiti Mukombwe, and Marie Grace Ntezimana

8. Deepfake and Image Forgery Detection in Forensic Analysis
T. C. Swetha Priya

9. Explainable AI in Forensic Image Analysis: Ensuring Interpretability and Transparency in Forensic AI Models
Wasswa Shafik

10. Analytical Frameworks for Forensic Imaging: From Matrix Operations to Statistical Inference
A. Neelima, P. Narender, G. V. S. S. Sarma, N. Hari Kumar, Gurusampath Kumar A., S. M. Faheem, and Murthy Chavali

11. Forgery and Tampering Detection: Passive and Active Techniques
J. Sudarvel, R. Velmurugan, Ravi Thirumalaisamy, and N. T. Shrie Bhubaneswari

12. Multispectral and Hyperspectral Imaging for Forensics: Applications in Forensic Analysis
N. G. Kalyani Reddy and Seifedine Kadry

13. Forensic Image Processing: Advanced Techniques in Detection
Anup P. Bhat, Kishor G. Rewtakr, Devidas S. Chavhan, and Sanjay J. Dhoble

14. Image Integrity Assessment Using Passive Statistical Analysis and Active Forensic Markers
R. Thamodiran, Ravi Thirumalaisamy, J. Sudarvel, and R. Velmurugan

15. Biometric Image Processing for Forensic Identification
Madeha Memon, Abdul Qayoom, Shahnawaz Talpur, and Cezar Sanin

16. AI in Forensics: Machine Learning and Deep Learning Methods for Image Denoising
A. Beena Godbin, SreeKrishna M., S. Vimalochana, and Prathap Mani

Index

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