How can I defend against adversarial attacks in AI?

How can I defend against adversarial attacks in AI?

Defending against adversarial attacks involves several strategies, such as adversarial training, input preprocessing, and model regularization.

What is adversarial training?

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

Are there specific algorithms for detecting adversarial examples?

Yes, there are algorithms like feature squeezing, input transformation, and ensemble methods that can help detect adversarial examples.

How effective are these defenses?

The effectiveness varies; some defenses can significantly improve robustness, while others may only provide limited protection.

Can you recommend any resources for further reading?

Sure! I recommend looking into research papers on adversarial machine learning and resources from conferences like NeurIPS or ICML.