Adversarial Robustness: Defending Against Model Attacks
Adversarial examples exploit model vulnerabilities with imperceptible perturbations. This guide implements practical defense mechanisms.
Adversarial Attack Generation
Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD):
Adversarial Training
The most effective defense trains on adversarial examples:
Certified Defenses
Randomized smoothing provides provable robustness guarantees:
Detection-Based Defense
Identify adversarial examples before classification:
Warnings ⚠️
No Perfect Defense: All current defenses have been broken. The 2034 "Adversarial Crisis" revealed that even certified defenses failed under adaptive attacks.
Robustness-Accuracy Tradeoff: Robust models sacrifice clean accuracy. Expect 5-10% drop.
Transferability: Adversarial examples transfer between models, making blackbox attacks feasible.
Related Chronicles: The Adversarial Cascade (2034) - When coordinated attacks compromised critical systems
Tools: Foolbox, CleverHans, ART (Adversarial Robustness Toolbox)
Research: Madry et al. (adversarial training), Cohen et al. (randomized smoothing)