Randomized Smoothing

Randomized Smoothing is a technique in machine learning for certifying the robustness of neural networks against adversarial attacks. It works by adding random noise to input data and analyzing the model's predictions over multiple noisy samples to provide probabilistic guarantees of stability. This method transforms any base classifier into a smoothed classifier with provable robustness guarantees under Lp-norm bounded perturbations.

Also known as: Randomised Smoothing, Certified Robustness via Smoothing, RS, Smoothed Classifiers, Probabilistic Robustness Certification

Why learn Randomized Smoothing?

Developers should learn Randomized Smoothing when building secure AI systems, especially in safety-critical applications like autonomous vehicles, medical diagnosis, or financial fraud detection where adversarial examples could cause harmful failures. It provides a practical way to certify model robustness without retraining, making it valuable for deploying reliable machine learning models in adversarial environments.

Learning Resources

• docs: Certified Adversarial Robustness via Randomized Smoothing
• video: Randomized Smoothing for Certified Robustness - MIT Lecture
• tutorial: Robust ML: Randomized Smoothing Tutorial
• tutorial: Implementing Randomized Smoothing in PyTorch
• docs: Certified Robustness to Adversarial Examples with Differential Privacy