Adversarial Robustness, in 5 Readable Parts
A shorter, more readable 5-part path through adversarial robustness: motivation, theory, attacks, defenses, and high-stakes deployment.
Adversarial-Learning
Why AI Can Be Brilliant but Fragile
Why high-performing AI systems can still fail under tiny perturbations, and why that fragility matters.
What Robustness Really Means
A theory-first guide to Bayes error, gradients, and loss landscapes as the foundation for robustness.
How Adversarial Attacks Evolved
From FGSM and PGD to 3D attacks, adversarial viewpoints, and explainability attacks.
How We Defend Models Against Adversarial Attacks
A compact map of the main defense routes: adversarial training, data-centric methods, certification, and efficient purification.
Robustness in Modern Models and High-Stakes Settings
Why robustness becomes a systems problem in modern architectures and high-cost applications.