Adversarial Attacks in Machine Learning: A Complete Guide

Описание: Dive deep into the world of adversarial attacks in machine learning—where cunning perturbations can trick even the strongest neural networks!

🔍 In this video, you'll learn:
✅ What Are Adversarial Attacks? – Understand adversarial examples, threat models, and attack objectives.

✅ Common Attack Methods – Explore Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Carlini–Wagner (CW) attacks, and more.

✅ Hands‑On Python Demo – Code walkthrough showing how to craft and apply FGSM / PGD on a sample CNN.

✅ Defense Mechanisms – Learn about adversarial training, defensive distillation, gradient masking, and certified defenses.

✅ Measuring Model Robustness – Tools and metrics like robust accuracy, L∞/L₂ norms, and adversarial benchmarks.

✅ Real‑World Examples – See how adversarial examples affect image recognition, autonomous driving, and security.

✅ Future Trends – Emerging defense research, adversarial certification, and GAN-based attack strategies.

By the end of this video, you’ll understand how and why adversarial attacks happen, how to implement them practically, and how to build robust and secure ML models! 🛡️

🔗 Resources & Links

📄 Key papers: “Explaining and Harnessing Adversarial Examples”, “Towards Evaluating the Robustness…”

🧰 GitHub demo repo: [LinkToRepo]

🎥 Related videos: “Defining Model Robustness”, “Certified Defenses Explained”

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