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Machine Learning Security Principles

You're reading from   Machine Learning Security Principles Keep data, networks, users, and applications safe from prying eyes

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804618851
Length 450 pages
Edition 1st Edition
Languages
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Author (1):
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John Paul Mueller John Paul Mueller
Author Profile Icon John Paul Mueller
John Paul Mueller
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Table of Contents (19) Chapters Close

Preface 1. Part 1 – Securing a Machine Learning System
2. Chapter 1: Defining Machine Learning Security FREE CHAPTER 3. Chapter 2: Mitigating Risk at Training by Validating and Maintaining Datasets 4. Chapter 3: Mitigating Inference Risk by Avoiding Adversarial Machine Learning Attacks 5. Part 2 – Creating a Secure System Using ML
6. Chapter 4: Considering the Threat Environment 7. Chapter 5: Keeping Your Network Clean 8. Chapter 6: Detecting and Analyzing Anomalies 9. Chapter 7: Dealing with Malware 10. Chapter 8: Locating Potential Fraud 11. Chapter 9: Defending against Hackers 12. Part 3 – Protecting against ML-Driven Attacks
13. Chapter 10: Considering the Ramifications of Deepfakes 14. Chapter 11: Leveraging Machine Learning for Hacking 15. Part 4 – Performing ML Tasks in an Ethical Manner
16. Chapter 12: Embracing and Incorporating Ethical Behavior 17. Index 18. Other Books You May Enjoy

Creating a deepfake computer setup

Creating a deepfake requires building serious models, using specialized software on systems that have more than a little computing horsepower. The system used for testing and in the screenshots for this chapter is more modest. It has an Intel i7 processor, 24 GB of RAM, and an NVidia GeForce GTX 1660 Super GPU. This system is used to ensure that the examples will run in a reasonable amount of time, with reasonable being defined as building a model in about half an hour or less. The example as a whole will require more time, likely in the hour range. The following sections will help you install a TensorFlow setup that you can use for autoencoder and GAN development without too many problems, and help you test your setup to ensure it actually works.

Installing TensorFlow on a desktop system

Desktop developers may already have TensorFlow installed, but if you’re not sure then you likely don’t. The technique for creating the advanced...

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