Ultimate Deepfake Detection Using Python Master Deep Learning Techniques like CNNs GANs and Transformers to Detect Deepfakes in Images Audio and Videos Using Python 1st Edition by Dr Nimrita Koul ISBN 8197953422 9788197953422 by Dr. Nimrita Koul instant download after payment.

Ultimate Deepfake Detection Using Python Master Deep Learning Techniques like CNNs GANs and Transformers to Detect Deepfakes in Images Audio and Videos Using Python 1st Edition by Dr Nimrita Koul - Ebook PDF Instant Download/Delivery: 8197953422, 9788197953422
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ISBN 10: 8197953422 
ISBN 13: 9788197953422
Author: Dr Nimrita Koul

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.

Ultimate Deepfake Detection Using Python Master Deep Learning Techniques like CNNs GANs and Transformers to Detect Deepfakes in Images Audio and Videos Using Python 1st Table of contents:

1. Introduction to Generative AI and Deepfake Technology
Introduction
Structure
Generative AI
Artificial Intelligence
Machine Learning
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement Learning
Deep Learning
Training Deep Neural Networks
Deep Neural Network Architectures
Deep Learning Architectures for Generative AI
Generative Adversarial Networks (GANs)
Generator
Generator Training
Discriminator
Discriminator Training
Applications of Generative Adversarial Networks
Variational Autoencoders
Autoencoders
Variational Autoencoder (VAE)
Training of VAE
Applications of VAEs
Transformer Model
Transformer Architecture
Self-Attention Mechanism
Deepfakes
History
Towards Developing Robust Deepfake Detection Techniques
Conclusion
References
2. Deepfake Detection Principles and Challenges
Introduction
Structure
Deepfakes
Identifying Fake Content
General Architecture of a Deepfake Detection System
Challenges in Deepfake Detection
Conclusion
3. Ethical Considerations with the Use of Deepfakes
Introduction
Structure
Understanding Deepfakes
Deleterious Effects of Deepfake Technology
Identity Theft of the Individuals
Attempts at Regulation of Deepfakes Across the World
Conclusion
References
4. Setting Up your Machine for Deepfake Detection using Python
Introduction
Structure
Deepfake Detection
Python for Deepfake Detection
System Requirements for Running Python
Installing Python
Installing Standard/Native Python
Installing for Windows
Installing Python on Linux/Unix
Installing on macOS
Creating a Virtual Environment in Standard Python
Installing Anaconda Python
Installing Anaconda for Windows
Installing Anaconda for Linux/Unix
Installing Anaconda for macOS
Creating a Virtual Environment in Anaconda
Deep Learning Libraries
NumPy (Numerical Python)
Pandas
Installation
Matplotlib
Scikit learn
TensorFlow
Keras
Installing Keras 3
PyTorch
Installing PyTorch 2.3
OpenCV
Installing OpenCV
PyAudio
Installing PyAudio
Librosa
Installing Librosa
Ffmpeg
Conclusion
5. Deepfake Datasets
Introduction
Structure
The Threat of Deepfakes
Deepfake Detection Models
Deepfake Detection Datasets
Summary of Deepfake Datasets
Techniques for Data Annotation and Labeling
Importance of Annotations and Labeling of Datasets
Annotations and Labeling for Image and Video Datasets
Annotations and Labeling for Speech and Audio Datasets
Ethical Considerations and Challenges in Deepfake Datasets
Conclusion
References
6. Techniques for Deepfake Detection
Introduction
Structure
The Need for Deepfake Detection
Evolution of Deepfake Generation Techniques
Generation of Deepfakes
Tools for Deepfake Detection
Few Commercial Tools for Deepfake Detection
Few Opensource Tools for Deepfake Detection
Techniques for Deepfake Detection
Convolutional Neural Networks
Layers in a Convolutional Neural Network
Convolutional Layer
Pooling
Fully Connected Layer
XceptionNet Architecture
Recurrent Neural Networks
Generative Adversarial Networks
Variational Autoencoders
Capsule Networks
Attention Mechanism
Transformer Model
Conclusion
References
7. Detection of Deepfake Images
Introduction
Structure
Deepfake Images
Categories of Face Manipulation for Creation of Deepfakes
Techniques for Detection of Deepfake Images
Traditional Methods
Active Methods
Passive Methods
1. Face Recognition and Identification of Key Facial Landmarks
2. Analysis of Inconsistencies in Image
3. Analysis of Statistical Anomalies in Image Data
4. Deep Learning-Based Deepfake Image Detection
Building a CNN-Based Deepfake Image Detection System
Code:
Evaluation Metrics for Deepfake Image Detection Systems
Conclusion
References
8. Detection of Deepfake Video
Introduction
Structure
The Staggering Increase in Deepfakes
Deepfake Video
Types of Manipulations Used to Create Deepfake Videos
Techniques for Detection of Deepfake Videos
Building a Deepfake Video Detection System
Evaluation Metrics for Deepfake Video Detection Systems
Conclusion
References
9. Detection of Deepfake Audio
Introduction
Structure
Deepfake Audio
Generation of Deepfake Audio
Techniques for Detection of Deepfake Audio
Building a Deepfake Audio Detection System
Code
Conclusion
References
10. Case Study in Deepfake Detection
Introduction
Structure
Deepfake Detection Challenge (DFDC)
Datasets for the Competition
Deepfake Detection Challenge Solution by Selim Seferbekov
Neural Networks Models Used
For Face Detection in Each Video Frame
For Encoding
Data Preparation Steps
Techniques Applied to Improve the Model Performance
Data Augmentation
Training Hardware
Python Implementation of the Solution
Open Issues in Deepfake Detection
Recent Advancements in Deepfake Detection
Future Research Directions
Conclusion
References
Index

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Tags: Dr Nimrita Koul, Deepfake, Detection