Home Blog Learn

All Courses

Advanced Adversarial Machine Learning

Chapter 1: Foundations of Adversarial ML Security

Review of Machine Learning Security Vulnerabilities

Threat Models in Machine Learning

Attack Surfaces: Training vs. Inference

Mathematical Formulation of Adversarial Examples

Taxonomy of Adversarial Attacks

Overview of Defense Strategies

Chapter 2: Advanced Evasion Attacks

Gradient-Based Attacks: FGSM, BIM, PGD Analysis

Optimization-Based Attacks: Carlini & Wagner Methods

Score-Based Attack Techniques

Decision-Based Attack Techniques

Transferability of Adversarial Examples

Attacking Ensemble Models

Implementing Evasion Attacks: Hands-on Practical

Chapter 3: Data Poisoning and Backdoor Attacks

Poisoning Attack Strategies: Availability vs Integrity

Targeted Data Poisoning Techniques

Backdoor Attack Mechanisms and Trigger Design

Clean-Label Poisoning Attacks

Analyzing Poisoning Impact on Model Training

Crafting Data Poisoning Attacks: Hands-on Practical

Chapter 4: Model Inference and Privacy Attacks

Membership Inference Attacks: Theory and Methods

Attribute Inference Techniques

Model Inversion and Reconstruction Attacks

Model Stealing: Functionality Extraction Methods

Relationship to Differential Privacy

Implementing Membership Inference: Hands-on Practical

Chapter 5: Robust Defense Mechanisms

Adversarial Training: Principles and Variations

Certified Defenses: Randomized Smoothing

Input Transformation Defenses

Gradient Masking and Obfuscation Issues

Defending Against Poisoning and Backdoors

Implementing Adversarial Training: Hands-on Practical

Chapter 6: Evaluating Model Robustness

Metrics for Adversarial Robustness

Benchmarking Tools and Frameworks

Adaptive Attacks: Evaluating Defenses Properly

Security Evaluations under Different Threat Models

Interpreting Robustness Evaluation Results

Setting up Robustness Benchmarks: Hands-on Practical

Chapter 7: Adversarial Examples in Specific Domains

Adversarial Attacks on Computer Vision Models

Generating Adversarial Text for NLP Models

Attacks on Reinforcement Learning Agents

Physical Adversarial Attacks

Domain-Specific Attack Considerations

Generating Adversarial Text: Practice

Advanced Adversarial Machine Learning

Prerequisites: Strong foundation in machine learning theory (classification, optimization), deep learning concepts (CNNs, RNNs), and proficiency in Python with ML libraries (e.g., TensorFlow/PyTorch, Scikit-learn).

Level: Advanced

What You'll Learn

Advanced Attack Implementation
Implement sophisticated evasion attacks (C&W, PGD) and data poisoning strategies.
Defense Mechanisms
Apply and analyze advanced defense techniques like adversarial training and certified defenses.
Model Inference Attacks
Understand and execute membership inference, attribute inference, and model stealing attacks.
Robustness Evaluation
Rigorously evaluate model security using standard benchmarks and adaptive attack strategies.
Domain-Specific Adversarial ML
Analyze adversarial threats specific to domains like computer vision and natural language processing.
Practical Implementation
Gain hands-on experience using frameworks like ART or CleverHans for attack and defense simulation.

© 2025 ApX Machine Learning

;