This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area.

Biometric Systems provides practitioners with an overview of the principles and methods needed to build reliable biometric systems. It covers three main topics: key biometric technologies, design and management issues, and the performance evaluation of biometric systems for personal verification/identification. The four most widely used technologies are focused on - speech, fingerprint, iris and face recognition. Key features include: in-depth coverage of the technical and practical obstacles which are often neglected by application developers and system integrators and which result in shortfalls between expected and actual performance; and protocols and benchmarks which will allow developers to compare performance and track system improvements.

This book provides a collection of the state-of-the-art research attempts to tackle the challenges in image and signal processing from various novel and potential research perspectives. The book investigates feature extraction techniques, image enhancement methods, reconstruction models, object detection methods, recommendation models, deep and temporal feature analysis, intelligent decision support systems, and autonomous image detection models. In addition to this, the book also looks into the potential opportunities to monitor and control the global pandemic situations. Image processing technology has progressed significantly in recent years, and it has been commercialized worldwide to provide superior performance with enhanced computer/machine vision, video processing, and pattern recognition capabilities. Meanwhile, machine learning systems like CNN and CapsNet get popular to provide better model hierarchical relationships and attempts to more closely mimic biological neural organization. As machine learning systems prosper, image processing and machine learning techniques will be tightly intertwined and continuously promote each other in real-world settings. Adopting this trend, however, the image processing researchers are faced with few image reconstruction, analysis, and segmentation challenges. On the application side, the orientation of the image features and noise removal has become a huge burden.

Face detection and recognition are the nonintrusive biometrics of choice in many security applications. Examples of their use include border control, driver's license issuance, law enforcement investigations, and physical access control.Face Detection and Recognition: Theory and Practice elaborates on and explains the theory and practice of face de

The widespread use of information and communications technology (ICT) has created a global platform for the exchange of ideas, goods and services, the benefits of which are enormous. However, it has also created boundless opportunities for fraud and deception. Cybercrime is one of the biggest growth industries around the globe, whether it is in the form of violation of company policies, fraud, hate crime, extremism, or terrorism. It is therefore paramount that the security industry raises its game to combat these threats. Today's top priority is to use computer technology to fight computer crime, as our commonwealth is protected by firewalls rather than firepower. This is an issue of global importance as new technologies have provided a world of opportunity for criminals. This book is a compilation of the collaboration between the researchers and practitioners in the security field; and provides a comprehensive literature on current and future e-security needs across applications, implementation, testing or investigative techniques, judicial processes and criminal intelligence. The intended audience includes members in academia, the public and private sectors, students and those who are interested in and will benefit from this handbook.

Digital transformation in organizations optimizes the business processes but also brings additional challenges in the form of security threats and vulnerabilities. Cyberattacks incur financial losses for organizations and can affect their reputations. Due to this, cybersecurity has become critical for business enterprises. Extensive technological adoption in businesses and the evolution of FinTech applications require reasonable cybersecurity measures to protect organizations from internal and external security threats. Recent advances in the cybersecurity domain such as zero trust architecture, application of machine learning, and quantum and post-quantum cryptography have colossal potential to secure technological infrastructures. The Handbook of Research on Cybersecurity Issues and Challenges for Business and FinTech Applications discusses theoretical foundations and empirical studies of cybersecurity implications in global digital transformation and considers cybersecurity challenges in diverse business areas. Covering essential topics such as artificial intelligence, social commerce, and data leakage, this reference work is ideal for cybersecurity professionals, business owners, managers, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

TAGLINE Deepfake Detection Unlocked: Python Approaches for Deepfake Images, Videos, Audio Detection. KEY FEATURES ● Comprehensive and graded approach to Deepfake detection using Python and its libraries. ● Practical implementation of deepfake detection techniques using Python. ● Hands-on chapters for detecting deepfake images, videos, and audio. ● Covers Case study for providing real-world application of deepfake detection. DESCRIPTION In today's digital world, mastering deepfake detection is crucial, with deepfake content increasing by 900% since 2019 and 96% used for malicious purposes like fraud and disinformation. "Ultimate Deepfake Detection with Python" equips you with the skills to combat this threat using Python’s AI libraries, offering practical tools to protect digital security across images, videos, and audio. This book explores generative AI and deepfakes, giving readers a clear understanding of how these technologies work and the challenges of detecting them. With practical Python code examples, it provides the tools necessary for effective deepfake detection across media types like images, videos, and audio. Each chapter covers vital topics, from setting up Python environments to using key datasets and advanced deep learning techniques. Perfect for researchers, developers, and cybersecurity professionals, this book enhances technical skills and deepens awareness of the ethical issues around deepfakes. Whether building new detection systems or improving current ones, this book offers expert strategies to stay ahead in digital media security. WHAT WILL YOU LEARN ● Understand the fundamentals of generative AI and deepfake technology and the potential risks they pose. ● Explore the various methods and techniques used to identify deepfakes, as well as the obstacles faced in this field. ● Learn to use essential datasets and label image, video, and audio data for building deepfake detection models. ● Apply advanced machine learning models like CNNs, RNNs, GANs, and Transformers for deepfake detection. ● Master active and passive methods for detecting face manipulation and build CNN-based image detection systems. ● Detect manipulations in videos, develop a detection system, and evaluate its performance using key metrics. ● Build and implement a practical deepfake detection system to understand how these techniques are applied in real-world scenarios. WHO IS THIS BOOK FOR? This book is tailored for anyone interested in deepfake detection using Python. Whether you're a researcher, developer, or cybersecurity professional, this guide provides the essential knowledge and skills. A basic understanding of Python and machine learning is helpful, but no prior experience in deepfakes is required. TABLE OF CONTENTS 1. Introduction to Generative AI and Deepfake Technology 2. Deepfake Detection Principles and Challenges 3. Ethical Considerations with the Use of Deepfakes 4. Setting Up your Machine for Deepfake Detection using Python 5. Deepfake Datasets 6. Techniques for Deepfake Detection 7. Detection of Deepfake Images 8. Detection of Deepfake Video 9. Detection of Deepfake Audio 10. Case Study in Deepfake Detection Index