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Program
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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
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)
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
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
Chapter 5.1: Case Studies: Attacks on Facial Recognition, NLP, and Healthcare Models
Chapter 5.2: Adversarial Threats in Federated Learning and Edge AI