PacktPub | Securing Your AI and Machine Learning Systems [Video] [FCO]

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  • TypeTutorials
  • LanguageEnglish
  • Total size772.3 MB
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  • Last checkedFeb. 06th '20
  • Date uploadedFeb. 06th '20
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Author : Alexander Polyakov
Released : 22 Jan 2020
Duration : 2 hours 10 minutes
Course Source : https://www.packtpub.com/in/data/securing-your-ai-and-machine-learning-systems-video

Design secure AI/ML solutions

Learn

• Design secure AI solution architectures to cover all aspects of AI security from model to environment
• Create a high-level threat model for AI solutions and choose the right priorities against various threats
• Design specific security tests for image recognition systems
• Test any AI system against the latest attacks with the help of simple tools
• Learn the most important metrics to compare various attacks and defences
• Deploy the right defence methods to protect AI systems against attacks by comparing their efficiency
• Secure your AI systems with the help of practical open-source tools

About

Artificial Intelligence (AI) is literally eating software as more and more solutions become ML-based. Unfortunately, these systems also have vulnerabilities; but, compared to software security, few people are really knowledgeable about this area. If it’s impossible to secure AI against cyberattacks, there will be no AI-based technologies, such as self-driving cars, and yet another “AI winter” will soon be on us.

This course is almost certainly the first public, online, hands-on introduction to the future perspectives of cybersecurity and adopts a clear and easy-to-follow approach. In this course, you will learn about high-level risks targeting AI/ML systems. You will design specific security tests for image recognition systems and master techniques to test against attacks. You will then learn about various categories of adversarial attacks and how to choose the right defense strategy.

By the end of this course, you will be acquainted with various attacks and, more importantly, with the steps that you can take to secure your AI and machine learning systems effectively. For this course, practical experience with Python, machine learning, and deep learning frameworks is assumed, along with some basic math skills.

All the code and supporting files for this course are available on GitHub at:

https://github.com/PacktPublishing/Securing-Your-AI-and-Machine-Learning-Systems

Features
• Gain practical experience with various open-source tools such as ART (Adversarial Robustness Toolkit) and DeepSec, developed to test machine learning algorithms for security
• Learn to design secure AI solutions depending on risks that are typical for your application with the help of a unique approach
• Understand the attacks and different approaches for securing various AI/ML systems

ISBN 9781838826451



Files:

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1 - Machine Learning Security
  • 01 - The Course Overview.mp4 (40.9 MB)
  • 02 - Introduction to ML Security.mp4 (29.9 MB)
  • 03 - Setting Up the Environment.mp4 (15.0 MB)
2 - Security Test Using Adversarial Attack
  • 04 - Introduction to Machine Learning Tasks.mp4 (26.0 MB)
  • 05 - Attacks Against ML with Examples.mp4 (7.3 MB)
  • 06 - Categories of ML Tasks and Attacks.mp4 (9.9 MB)
  • 07 - Attacks on Classification and How They Work.mp4 (5.4 MB)
  • 08 - Practical Example ..Adversarial Challenge.mp4 (17.0 MB)
3 - Build a Threat Model and Learn Different Attacks on AI
  • 09 - Most Common AI Solutions and Threats.mp4 (45.9 MB)
  • 10 - Confidentiality, Availability, and Integrity Attacks.mp4 (5.1 MB)
  • 11 - Poisoning Attacks, Privacy, and Backdoor Attacks Theory.mp4 (9.0 MB)
  • 12 - Practical Poisoning Attacks.mp4 (12.9 MB)
  • 13 - Practical Privacy Attacks.mp4 (15.2 MB)
  • 14 - Practical Backdoor Attacks.mp4 (17.1 MB)
4 - Testing Image Classification
  • 15 - Building an Image Classification Task and Its Peculiarities.mp4 (5.5 MB)
  • 16 - Adversarial Attacks and Their Distinctive Features.mp4 (9.7 MB)
  • 17 - White-Box Adversarial with Example.mp4 (9.3 MB)
  • 18 - Grey-Box Adversarial with Example.mp4 (9.4 MB)
  • 19 - Black-Box Adversarial with Example.mp4 (9.5 MB)
5 - Compare Various Attacks
  • 20 - Adversarial Attacks Metrics and White-Box Adversarial Attacks.mp4 (7.3 MB)
  • 21 - BIM Attack Practical Configuration.mp4 (16.1 MB)
  • 22 - CW Attack Practical Configuration.mp4 (10.5 MB)
  • 23 - DeepFool Attack Practical Configuration.mp4 (9.7 MB)
  • 24 - PGD Attack Practical Configuration.mp4 (15.2 MB)
  • 25 - Comparing Metrics and Choosing the Best Attack.mp4 (11.9 MB)
6 - Choosing the Right Defense
  • 26 - Introduction to Various Defense Approaches to Adversarial Attacks.mp4 (7.3 MB)
  • 27 - The Current State of Defenses.mp4 (2.7 MB)
  • 28 - Testing Practical Defense from Adversarial Training Category.mp4 (11.8 MB)
  • 29 - Testing Practical Defence from Modified Input Category.mp4 (8.3 MB)
  • 30 - Testing Practical Defence from Modified Model Category.mp4 (4.4 MB)
  • 31 - Comparing Defence Approaches and Choosing the Best Defence.mp4 (15.2 MB)
7 - Summary and Future Trends
  • 32 - Combining Everything Together.mp4 (7.3 MB)
  • 33 - An Approach to Testing AI Solutions.mp4 (3.0 MB)
  • 34 - Preparing the Environment.mp4 (9.9 MB)
  • 35 - Importing the Models.mp4 (8.0 MB)
  • 36 - Testing the Attacks.mp4 (13.0 MB)
  • 37 - Choosing the Defenses.mp4 (15.0 MB)
  • 38 - The Future of AI Attacks.mp4 (9.0 MB)
  • 39 - Sources and Recommendations.mp4 (16.5 MB)
  • 40 - Conclusions and Best Wishes.mp4 (9.0 MB)
  • 9781838826451_Code.zip (261.0 MB)

Code:

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