Explain FGSM (Fast Gradient Sign Method) and how it bypasses ML classifiers.

FGSM (Goodfellow 2014) — generates adversarial example by adding small perturbation in direction of gradient sign of loss function. Math: x_adv = x + ε · sign(∇_x J(θ, x, y)) where ε is perturbation magnitude. Result: tiny modification to input causes classifier to misclassify with high confidence. Real-world impact: malware classifier marks malware as benign with single-byte changes; image classifier confidently mislabels stop sign as 100mph speed limit. Defence: adversarial training (train on perturbed examples), randomised smoothing, certified defences.