Outline

Execute evasion attacks on diverse models (image, text, tabular). • Implement poisoning attacks during model training. • Perform model inversion and membership inference attacks. • Utilize leading adversarial ML tools (Foolbox, ART, CleverHans).

Outline

Apply adversarial training techniques to enhance resilience. • Employ input preprocessing and gradient masking for defense. • Explore certified defenses and formal security guarantees. • Evaluate model robustness using specialized metrics.

Outline

Design secure data pipelines and ensure label integrity. • Identify and mitigate attack surfaces in model deployment. • Conduct threat modeling for machine learning systems. • Implement secure MLOps practices and monitoring pipelines.

Outline

Analyze real-world case studies of attacks on AI systems. • Investigate adversarial threats in federated learning and Edge AI. • Address legal, ethical, and compliance risks in AI security. • Practice AI red teaming and offensive security testing.

Outline

Design and conceptualize an adversarial attack scenario. • Simulate and evaluate robust defense mechanisms. • Present a final capstone project showcasing applied skills. • Examine future directions in AI security and regulation.