What are some effective defense techniques against adversarial examples?

What are some effective defense techniques against adversarial examples?

There are several techniques to defend against adversarial examples, including adversarial training, defensive distillation, and input preprocessing.

Can you explain adversarial training?

Adversarial training involves augmenting the training dataset with adversarial examples, helping the model learn to recognize and resist them.

What is defensive distillation?

Defensive distillation is a technique where a model is trained to predict the softmax probabilities of another model, making it more robust to adversarial attacks.

Are there any preprocessing techniques?

Yes, input preprocessing techniques like feature squeezing and JPEG compression can help reduce the impact of adversarial perturbations.

How effective are these techniques in practice?

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