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Adversarial Machine Learning

Begin your career in AI security by simply mastering the offensive & defensive strategies required for secure modern adversarial machine learning systems.

(ADV-ML.AU1) / ISBN : 979-8-90059-016-5
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About This Course

Have you ever been tasked with the deploying intelligent systems, only to find traditional security protocols fail to protect against ML vulnerabilities? However, the assumption of the clean, uncorrupted input data is dangerously violated in the high-stakes environments, where the attackers intentionally supply fabricated data.

This specialized Adversarial machine learning approach offers the rigorous, hands-on foundation required to build & defend models against sophisticated threats such as data poisoning attacks & complex evasion attacks.

Master AI red teaming using industry-standard tools, which includes of Adversarial Robustness Toolbox, allowing you to assess & strengthen machine learning robustness. The following program delivers practical skills for securing the entire secure ML workflow MLOps pipeline, preparing you to become an asset in the field of adversarial AI. 

Skills You’ll Get

  • Adversarial Machine Learning: Learn the core principles of Adversarial machine learning, understanding the fundamental differences between attack types, including data poisoning attacks, model extraction, and Trojan attacks that compromise model integrity or privacy. 
  • Adversarial Learning Frameworks: Master the application of specialized attack frameworks such as Fast Gradient Sign Method & projected Gradient Descent to generate potent Adversarial examples and test for ML vulnerabilities. 
  • Adversarial Security Mechanisms: Implementing robust defenses, which include Adversarial training, Defensive distillation, and differential privacy, ensuring your models achieve high machine learning robustness against both digital and physical world adversarial examples.
  • Stochastic Game Illustration in Adversarial Deep Learning: Analyzing the dynamic, competitive interaction between the attacker and defender using the game theoretical models to formulate advanced defense strategies & secure systems against targeted adversarial AI threats. 
Download Course Outline

1

Preface

2

Adversarial Machine Learning

  • Adversarial Learning Frameworks
  • Adversarial Security Mechanisms
  • Stochastic Game Illustration in Adversarial Deep Learning
3

Adversarial Deep Learning

  • Learning Curve Analysis for Supervised Machine Learning
  • Adversarial Loss Functions for Discriminative Learning
  • Adversarial Examples in Deep Networks
  • Adversarial Examples for Misleading Classifiers
  • Generative Adversarial Networks
  • Generative Adversarial Networks for Adversarial Learning
  • Transfer Learning for Domain Adaptation
4

Adversarial Attack Surfaces

  • Security and Privacy in Adversarial Learning
  • Feature Weighting Attacks
  • Poisoning Support Vector Machines
  • Robust Classifier Ensembles
  • Robust Clustering Models
  • Robust Feature Selection Models
  • Robust Anomaly Detection Models
  • Robust Task Relationship Models
  • Robust Regression Models
  • Adversarial Machine Learning in Cybersecurity
5

Game Theoretical Adversarial Deep Learning

  • Game Theoretical Learning Models
  • Game Theoretical Adversarial Learning
  • Game Theoretical Adversarial Deep Learning
  • Stochastic Games in Predictive Modeling
  • Robust Game Theory in Adversarial Learning Games
6

Adversarial Defense Mechanisms for Supervised Learning

  • Securing Classifiers Against Feature Attacks
  • Adversarial Classification Tasks with Regularizers
  • Adversarial Reinforcement Learning
  • Computational Optimization Algorithmics for Game Theoretical Adversarial Learning
  • Defense Mechanisms in Adversarial Machine Learning
7

Physical World Adversarial Attacks on Images and Texts

  • Adversarial Attacks on Images
  • Adversarial Attacks on Texts
  • Spam Filtering
8

Adversarial Perturbation for Privacy Preservation

  • Adversarial Perturbation for Privacy Preservation

1

Adversarial Machine Learning

  • Exploring the Adversarial Learning Framework
2

Adversarial Deep Learning

  • Understanding Adversarial Examples
  • Understanding a Black-Box Attack
3

Adversarial Attack Surfaces

  • Exploring Adversarial Attack Surfaces
4

Game Theoretical Adversarial Deep Learning

  • Analyzing Game-Theoretical Adversarial Interaction
5

Adversarial Defense Mechanisms for Supervised Learning

  • Understanding Adversarial Defense Mechanisms
6

Physical World Adversarial Attacks on Images and Texts

  • Understanding Spam Filtering
7

Adversarial Perturbation for Privacy Preservation

  • Understanding Adversarial Perturbation

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This course is essential for aspiring AI Security Analyst and ML Security Analyst, Machine Learning Engineers focused on deployment, and senior professionals responsible for developing Secure ML Workflow MLOps practices and infrastructure.

The course provides hands-on Performance Based labs to teach you how to craft sophisticated attack vectors, including both white-box attacks like PGD and FGSM, as well as stealthy Black-box attacks and model extraction techniques.

Yes; while the foundation focuses on core Adversarial Machine Learning (AML), advanced modules cover current high-relevance threats such as LLM Attacks and mitigating security concerns like Prompt injection and data leakage.

You will gain practical experience using the leading open-source security tool, the Adversarial Robustness Toolbox (ART), to implement defense strategies such as Adversarial training, defensive pre-processors, and certifying model robustness.

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Transform defense theories into verified skills with the AML certification program and become an expert in AI security.

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