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.

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.

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.

A passionate educator, Dr. Koul is committed to using AI to enhance education, particularly in remote and underserved areas. She has delivered numerous international workshops and seminars on Data Analysis, Machine Learning, Natural Language Processing, and Generative AI, and is a sought-after speaker at global conferences such as GHC2023 and WomenWhoConnect Forward 2021.

In addition to her academic pursuits, Dr. Koul is actively involved in mentoring and inspiring the next generation of technologists, particularly women in tech, through her role as an ambassador for Google Women Techmakers.

In this book, Ultimate Deepfake Detection Using Python, Dr. Koul combines her extensive knowledge of AI with practical Python programming to guide readers through the latest techniques in detecting deepfake videos. The book also explores recent advancements in the field, offering insights into the future directions of deepfake detection research.

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ABOUT TECHNICAL REVIEW

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Astha is a Senior Data Scientist at a top Fortune 10 company, where she designs recommendation engines for digital platforms to help customers find the right products and patients access the right health services and support. She also leads AI initiatives, including generative AI, and oversees the entire search portfolio across the app and web. Astha holds a master's in Analytics from the University of Minnesota and a B.Tech in Electronics and Communication from VIT University.

With nine years of experience in data science at tech companies like Oracle and Twilio, Astha is now applying her expertise to healthcare. With a background in healing and alternative therapies, she is researching how to integrate AI, health, and healing. Astha is passionate about empowering healthcare professionals with the knowledge and tools they need to excel. She recently contributed chapters to a book in collaboration with the Indian Fertility Society, focusing on AI in counseling for Assisted Reproduction Technology. As one of the 19 members of the national core team in India, she is leading the technology and AI platform for Counselor Empowerment Program (CEP), with a mission to provide data-driven AI systems to support counselors and patients in IVF centers across the country.

Her work is unique in that it merges traditional healing therapies and psychotherapy with artificial intelligence, using Python-based models built on anonymized data combined with technology like OpenAI and Gemini, and using architectures such as RAG to ensure contextual and reliable AI. This approach allows for hyper- personalized treatment plans for individuals while maintaining data privacy and accountability. In the context of IVF patients, this method aims to prevent additional burdens like depression, ensuring they receive comprehensive support during their challenging journey.

In addition to this, Astha is a speaker, author, and mentor. As a founding board member and vice president of Women Who Do Data, she helps women advance in data science. Astha has been recognized among the top 250 women globally in AI and ML and has received the Indian Achiever Award, Global Recognition Award, and Excellence in Applied Research Award.