How can we defend against adversarial attacks in AI?

How can we defend against adversarial attacks in AI?

Defending against adversarial attacks involves techniques like adversarial training, input preprocessing, and model robustness evaluation.

What is adversarial training?

Adversarial training is a method where the model is trained on both clean and adversarial examples to improve its robustness.

Are there any specific algorithms used for this?

Yes, algorithms like Projected Gradient Descent (PGD) and Fast Gradient Sign Method (FGSM) are commonly used in adversarial training.

How effective are these defenses?

The effectiveness varies; while they can significantly improve robustness, some advanced attacks can still bypass these defenses.

What are some emerging techniques in this field?

Emerging techniques include certified defenses, ensemble methods, and the use of generative models to create robust training data.