What are some effective defense strategies against adversarial attacks?

What are some effective defense strategies against adversarial attacks?

There are several strategies to defend against adversarial attacks, including adversarial training, input preprocessing, and model ensembling.

Can you explain adversarial training?

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

What is input preprocessing?

Input preprocessing techniques include normalizing inputs, applying transformations, or using denoising methods to reduce the impact of adversarial perturbations.

How does model ensembling work?

Model ensembling combines predictions from multiple models to improve robustness, as different models may respond differently to adversarial inputs.

Are there any other strategies I should know about?

Yes, other strategies include using defensive distillation, feature squeezing, and implementing robust optimization techniques.