Adversarial ML Attack & Defend Neural Networks

What you'll learn

⚡ Build a full PyTorch training and evaluation pipeline with reproducibility discipline
⚡ Implement the core white-box attack arsenal from scratch
⚡ Simulate realistic black-box threats against a live FastAPI inference service
⚡ Attack beyond evasion
⚡ Engineer real defenses and measure them honestly
⚡ Build ML governance evidence
⚡ Automate MLOps and supply-chain security
⚡ Deploy and operate production inference safely
⚡ Design for sovereignty
⚡ Deliver a capstone-grade Sovereign Adversarial ML Defense Platform

Requirements

❗ Knowledge: Basic Python (functions, running scripts) and basic terminal comfort. No prior deep learning, PyTorch, or adversarial ML experience required — Module 1 trains your first neural network from a bare workstation up. Basic familiarity with machine learning concepts (what training and accuracy mean) helps but isn't required — every concept is explained before it's used. No prior Kubernetes, MLOps, or DevSecOps experience needed — Modules 8–9 build those skills from scratch. Software (all free/open-source): Python 3.11+, Git, Docker. Python packages installed via pip in Lab 1: PyTorch, torchvision, scikit-learn, FastAPI, Uvicorn, pytest, MLflow, the Adversarial Robustness Toolbox (ART), Foolbox, Evidently, Great Expectations, Prometheus client, OpenTelemetry — all free and open-source. kubectl (optional, for Module 9's Kubernetes labs) — a local cluster, not a cloud account. Optional: Trivy and Cosign for supply-chain labs — the course provides graceful fallbacks if they aren't installed. Hardware: CPU-only is fully sufficient — this course intentionally uses a small, fast scikit-learn digits dataset (8x8 grayscale images) so every lab runs in seconds without a GPU. 5GB+ free disk space, 4GB+ RAM. No cloud account, no GPU, and no real production data required — every lab uses a safe, built-in benchmark dataset with no licensing or download concerns.

Description

This course contains the use of artificial intelligence.
We only charge a fee solely for the time invested in building this comprehensive curriculum.
The "98% Accuracy" Illusion
Every ML team has celebrated a model that hits 95%, 98%, even 99% test accuracy — and then deployed it without ever asking what happens when someone deliberately tries to break it. That's the gap adversarial machine learning exists to close: a model that performs beautifully on clean data can collapse under a perturbation so small a human can't even see it, can be poisoned during training by a handful of mislabeled examples, can leak its own training data through membership inference, or can be copied wholesale by an attacker who does nothing but query its public API a few hundred times. In 2026, these aren't research curiosities — they're operational risks sitting behind fraud detection, identity verification, healthcare triage, and content moderation systems.
This course closes that gap the only way that actually works: by making you build, attack, and defend a real neural network yourself, with every result measured and none of it assumed. You don't learn adversarial ML by reading about FGSM. You learn it by watching your own model's accuracy collapse under an attack you wrote, then engineering a defense and proving — with a shared evaluation harness — whether it actually helped.
The Solution: 100 Labs, One Growing AI Security Platform
Every lab follows the same Zero-Failure workflow: a pre-flight check, a Git checkpoint, and a documented rollback. By Lab 10 you'll have trained a baseline neural network, evaluated it, run your first FGSM attack, visualized the perturbation, built a basic defense, and turned the whole cycle into a regression test — a complete, working milestone before the deeper architecture arrives.
What's InsideModules 1–2 build your workstation and evaluation discipline: a reproducible PyTorch project, a safe benchmark dataset with integrity hashing and schema validation, config-driven training, MLflow experiment tracking, a reusable model factory, a centralized metric registry, and a clean-accuracy CI gate — because you can't measure robustness on a model you haven't first proven is trustworthy on clean data.
Module 3 builds the white-box attack arsenal properly: a reusable, tested FGSM function, an epsilon sweep showing the real robustness curve, targeted FGSM, the Basic Iterative Method, full Projected Gradient Descent, and a Carlini-Wagner-style margin-loss attack — then, critically, cross-validation of every result against Foolbox and ART, so you're never trusting custom attack code alone.
Module 4 simulates realistic attacker constraints: a live FastAPI inference service, score-based probing, decision-only boundary search that never sees confidence scores, query-budget accounting, API rate limiting, transfer attacks from a surrogate model against the hidden API, structured abuse logging, and query-burst detection.
Module 5 goes beyond evasion into the attack surface most courses skip entirely: label-flipping poisoning measured against clean test performance, a real backdoor trigger with pixel-statistics detection, model extraction via query harvesting into a surrogate model, membership inference privacy risk simulation, and privacy-aware hashed logging — all mapped into a MITRE ATLAS-style threat table and a formal abuse case register.
Module 6 engineers real defenses honestly: FGSM and PGD adversarial training evaluated against each other (not just against the weaker attack they were trained on), confidence-based rejection, entropy detection, input drift statistics, a randomized smoothing prototype, calibration and temperature scaling, and — the key discipline — one shared defense harness that tests every defense against the same attack suite, so no defense gets to claim victory against a weaker opponent than the others faced.
Module 7 builds the governance evidence real AI systems need: a dataset card and model card documenting intended use and known limitations, a formal risk register, and control maps translating your technical work into NIST AI RMF functions, EU AI Act-style requirements, and DORA-style operational resilience — plus an ML-BOM, a data retention policy, and a complete audit evidence bundle.
Module 8 automates the entire pipeline: a local CI script and a GitHub Actions-compatible workflow, a Docker inference image, container vulnerability scanning, SBOM generation, Cosign-style artifact signing with SHA-256 fallback, SLSA-style provenance metadata, a model promotion gate with explicit clean-accuracy and robustness thresholds, and a release manifest binding model, evidence, and rollback path together.
Module 9 deploys and operates the system like production: Kubernetes manifests with readiness probes, Prometheus metrics, OpenTelemetry tracing, defined SLOs (including asecurity SLO requiring adversarial regression to pass before promotion), local load testing, canary deployment manifests, and a full incident-and-rollback playbook with real kubectl rollout undo commands.
Module 10 closes with sovereignty and reproducibility: a local-first deployment profile that runs without proprietary managed services, a decentralized research reproducibility pack that lets another team independently verify your findings, and a formal capstone architecture design review before the final build.
The Climax: Lab 100 — The Sovereign Adversarial ML Defense Platform
Lab 100 runs the entire system end to end: local CI, the defense harness, the model promotion gate, ML-BOM and provenance generation, the release manifest, the audit bundle, and the research reproducibility pack — all committed under a final capstone Git tag. The evaluation is honest by design: you're required to document limitations, distinguish educational benchmarks from production claims, and explain the real tradeoffs between clean accuracy, robustness, privacy, latency, and cost. This isn't a notebook you close and forget — it's a complete, defensible AI security platform you built and measured yourself.
Why Enroll Now
The market signal is clear: 2026 job descriptions for AI security engineer, ML security engineer, MLOps engineer, and AI governance roles increasingly ask for exactly this blend — attack evaluation, defensive engineering, MLOps automation, supply-chain security, and compliance documentation — not just the ability to reproduce FGSM in a notebook. Every tool in this course is free and open-source (PyTorch, ART, Foolbox, MLflow, Prometheus, OpenTelemetry), runs entirely on CPU, and requires no cloud account — so the platform you build is genuinely yours, reproducible anywhere, and ready to anchor a portfolio that proves engineering discipline, not just familiarity with a research paper.

Who this course is for

⭐ The ML Engineer Who's Never Actually Attacked Their Own Model You've trained models that hit great accuracy numbers, but you've never measured what happens when someone deliberately tries to fool them. This course builds the full attack arsenal — FGSM through PGD, poisoning, extraction, membership inference — against a model you train yourself, so "robustness" becomes something you can measure instead of assume.
⭐ The Security Engineer Moving Into AI/ML Security You know threat modeling and red-teaming, but the ML-specific attack surface — gradient-based evasion, backdoor triggers, model extraction via API queries — is unfamiliar territory. This course builds every attack and its corresponding defense as testable, reproducible code, mapped to MITRE ATLAS-style threat categories, so you can speak both languages fluently.
⭐ The Senior MLOps/AI Platform Architect Building Governed AI Systems You're responsible for shipping models to production, and "the model works" isn't the same as "we can prove it's robust, monitored, auditable, and rollback-safe." This course builds the entire chain — CI-gated promotion, SBOM/ML-BOM, signed artifacts, NIST AI RMF and EU AI Act-style control maps — culminating in a capstone platform you could defend to a governance review board.

Homepage

https://www.udemy.com/course/adversarial-ml/?couponCode=MT260907G1B

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