Adversarial ML & Security Threats

Aim

Program Objectives

Program Structure

Week 1: Foundations of Adversarial Machine Learning
Module 1: Introduction to Adversarial ML

• Chapter 1.1: What is Adversarial ML?
• Chapter 1.2: Historical Context and Emerging Importance
• Chapter 1.3: Types of Adversarial Threats (White-box, Black-box, Gray-box)
• Chapter 1.4: Overview of Vulnerabilities in ML Pipelines

Module 2: Attacks Against ML Models

• Chapter 2.1: Evasion Attacks on Image, Text, and Tabular Models
• Chapter 2.2: Poisoning Attacks During Training
• Chapter 2.3: Model Inversion and Membership Inference
• Chapter 2.4: Tools and Libraries (Foolbox, ART, CleverHans)

Week 2: Defensive Strategies and Robust Model Design
Module 3: Making Models Robust

• Chapter 3.1: Adversarial Training Techniques
• Chapter 3.2: Input Preprocessing and Gradient Masking
• Chapter 3.3: Certified Defenses and Formal Guarantees
• Chapter 3.4: Evaluation Metrics for Robustness

Module 4: Security in the ML Lifecycle

• Chapter 4.1: Secure Data Pipelines and Label Integrity
• Chapter 4.2: Attack Surface in Model Deployment
• Chapter 4.3: Threat Modeling for ML Systems
• Chapter 4.4: Secure MLOps and Monitoring Pipelines

Week 3: Real-World Applications and Future Challenges
Module 5: Adversarial ML in Practice

• Chapter 5.1: Case Studies: Attacks on Facial Recognition, NLP, and Healthcare Models
• Chapter 5.2: Adversarial Threats in Federated Learning and Edge AI
• Chapter 5.3: Legal, Ethical, and Compliance Risks
• Chapter 5.4: AI Red Teaming and Offensive Testing

Module 6: Capstone and Emerging Trends

• Chapter 6.1: Design Your Own Adversarial Attack Scenario
• Chapter 6.2: Simulate and Evaluate Defense Mechanisms
• Chapter 6.3: Final Capstone Project Presentation
• Chapter 6.4: Future Directions – AI Security, Regulation, and Red-Blue Team Dynamics

Who Should Enrol?

Program Outcomes

Fee Structure

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What You’ll Gain