Название: Ultimate Deepfake Detection Using Python: Master Deep Learning Techniques like CNNs, GANs, and Transformers to Detect Deepfakes in Images, Audio, and Videos Using Python
Автор: Nimrita Koul
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 306
Язык: английский
Формат: epub (true)
Размер: 91.6 MB

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.

A prominent and most successful technique for building Artificial Intelligence is Machine Learning (ML) and its subsets - Deep Learning (DL), natural language processing (NLP), and Reinforcement Learning (RL). Machine learning uses data to learn patterns in it and then uses these patterns for prediction. However, there are alternative approaches to build AI. These approaches include Symbolic systems (Expert systems), Evolutionary algorithms, Logic-based systems, and their hybrids.

This book is structured into ten comprehensive chapters that guide you through the essential aspects of deepfake detection. It begins with the foundational concepts of generative models and deepfake technology, exploring the principles behind detecting deepfake media. You will then learn about Python as a powerful tool for deepfake detection, followed by an in-depth examination of the relevant datasets and deep learning approaches critical for building robust detection systems. The book provides detailed Python code for creating deepfake detection systems specifically tailored to images, videos, and audio. Finally, it culminates in a thorough case study of an award-winning deepfake detection system, offering practical insights and expert techniques.

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.

Contents: