01. Overview & Adversarial ML Threat Landscape
Explore the security paradigm of Machine Learning models, comparing classical software vulnerabilities with adversarial AI threats per NIST AI 100-2 and MITRE ATLAS.
02. Adversarial Input Robustness & Sensitivity
Deep technical analysis of adversarial evasion attacks, perturbation norms (L0, L2, L_inf), FGSM, PGD, Carlini & Wagner attacks, and robust adversarial training in PyTorch and TensorFlow.
03. Data Poisoning & Dataset Sanitization
Comprehensive guide to Data Poisoning, Clean-Label Attacks, Trojan Backdoors, Spectral Signature Sanitization, and Supply Chain Dataset Provenance.
04. Model Intellectual Property & Extraction Defense
Protecting proprietary machine learning models against model stealing (extraction), membership inference attacks, and data inversion using differential privacy (DP-SGD), logit truncation, and model watermarking.
05. Automated ML Security Tooling & Evaluation Frameworks
Comprehensive guide to automating ML security audits using the Linux Foundation Adversarial Robustness Toolbox (ART), Microsoft Counterfit, picklescan, modelscan, and CI/CD security pipelines.
06. Hands-On Audit Lab: PyTorch Model Security & Hardening
Self-contained hands-on lab: audit a PyTorch fraud detection model, execute FGSM/PGD evasion attacks, clean-label data poisoning, and model extraction, and apply PGD adversarial training, spectral sanitization, and secure API hardening.
07. References & Standards
Authoritative standards, benchmark leaderboards, foundational academic research papers, and open-source security toolkits for Machine Learning Security.
ML Model Security & Adversarial Attacks Guide
Comprehensive guide to Machine Learning Model Security: evaluating and defending predictive models and neural networks against data poisoning, adversarial input evasion, model stealing, membership inference, and supply chain attacks.