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. Book 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 you will 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. 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 About the Authors Dr. Nimrita Koul is an Associate Professor of Computer Science and Engineering at Reva University in Bangalore, Karnataka, India. With a PhD in Machine Learning and an academic and research career spanning over 19 years, she is an active researcher in the areas of Machine Learning, Natural Language Processing, and Generative AI. Dr. Koul is a senior member of IEEE and a member of ACM, and she has been the principal investigator for multiple research projects worth over 1.3 crores, funded by the Department of Science and Technology, Government of India. Her expertise has been recognized through several prestigious awards, including the Research Accelerator Award in 2021, the Jetson Nano Grant in 2020, and the IBM Generative AI Award in 2023.