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ADV-ML.AU1
Adversarial Machine Learning
Begin your career in AI security by simply mastering the offensive & defensive strategies required for secure modern adversarial machine learning systems.
- Practice in 36 Hands-On Labs — nothing to install
- 8 Interactive Lessons and 34 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
36 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Skills you'll get
What you will be able to do
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.
- 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.
Course Highlights
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8 Structured Lessons Comprehensive coverage of core course objectives
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36 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
8 Interactive Lessons · 34 topics01 Preface +
02 Adversarial Machine Learning 3 topics · 4 LiveLab +
- Adversarial Learning Frameworks
- Adversarial Security Mechanisms
- Stochastic Game Illustration in Adversarial Deep Learning
4 LiveLab in this lesson — see the labs panel →
03 Adversarial Deep Learning 7 topics · 8 LiveLab +
- 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
8 LiveLab in this lesson — see the labs panel →
04 Adversarial Attack Surfaces 10 topics · 8 LiveLab +
- 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
8 LiveLab in this lesson — see the labs panel →
05 Game Theoretical Adversarial Deep Learning 5 topics · 4 LiveLab +
- 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
4 LiveLab in this lesson — see the labs panel →
06 Adversarial Defense Mechanisms for Supervised Learning 5 topics · 6 LiveLab +
- 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
6 LiveLab in this lesson — see the labs panel →
07 Physical World Adversarial Attacks on Images and Texts 3 topics · 4 LiveLab +
- Adversarial Attacks on Images
- Adversarial Attacks on Texts
- Spam Filtering
4 LiveLab in this lesson — see the labs panel →
08 Adversarial Perturbation for Privacy Preservation 1 topics · 2 LiveLab +
- Adversarial Perturbation for Privacy Preservation
2 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
36 LiveLabs- Exploring the Adversarial Learning Framework
- Comparing Classifier Robustness Against Adversarial Attacks
- Evaluating Classifier Performance Under Gaussian Noise
- Simulating Stochastic Defender-Attacker Decisions
- Analyzing Learning Curves for Model Performance
- Evaluating Neural Network Robustness Using Perturbed Inputs
- Understanding Adversarial Examples
- Fooling a Neural Network with Tiny Perturbations
- Executing a Black-Box Attack Using ART
- Building and Training a Simple GAN
- Understanding a Black-Box Attack
- Evaluating Transfer Learning Across Different Data Domains
- Performing a Simple Dataset Poisoning Attack
- Analyzing Classifier Behavior Under Input Perturbations
- Evaluating Ensemble Learning Under Input Perturbations
- Evaluating Clustering Robustness Against Data Manipulation
- Testing Anomaly Detection Systems Against Evasion Attempts
- Evaluating Regression Models Under Feature Data Manipulation
- Exploring Adversarial Attack Surfaces
- Evaluating Adversarial Attacks on Time-Series Models
- Modeling Attacker and Defender Strategies Using Game Theory
- Identifying Suspicious Inputs Using Prediction Confidence
- Analyzing Game-Theoretical Adversarial Interaction
- Protecting an IDS Against Adversarial Inputs
- Defending Classifiers Against Feature Manipulation Attacks
- Improving Model Robustness Using Regularization Techniques
- Evaluating Simulated Reward Trends Under Perturbed Conditions
- Modeling Learner-Versus-Adversary Interactions
- Understanding Adversarial Defense Mechanisms
- Strengthening Models Using Feature Squeezing and Defensive Distillation
- Generating Adversarial Images to Mislead Classifiers
- Exploring Character-Level Perturbations in Text Classification
- Understanding Spam Filtering
- Evading and Strengthening Spam Filters Against Adversarial Messages
- Understanding Adversarial Perturbation
- Testing Model Robustness Across Multiple Models Under Noisy Inputs
03 / FAQs
Questions before you start
Who should take the Adversarial Machine Learning course?+
What level of attack crafting is covered?+
Does the training cover emerging threats like LLMs?+
What tools will I use to practice defense mechanisms?+
Keep going
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