What are some effective defenses against adversarial attacks in AI?

What are some effective defenses against adversarial attacks in AI?

There are several effective defenses against adversarial attacks, including adversarial training, input preprocessing, and model ensembling.

Can you explain adversarial training?

Adversarial training involves training the model on both clean and adversarial examples to improve its robustness.

What is input preprocessing?

Input preprocessing techniques include normalizing inputs, adding noise, or using transformations to make it harder for adversarial examples to succeed.

Are there any specific algorithms for model ensembling?

Yes, algorithms like bagging and boosting can be used for model ensembling to enhance robustness against adversarial attacks.

How effective are these defenses in real-world applications?

The effectiveness varies; while they can significantly improve robustness, no defense is foolproof against all types of adversarial attacks.