What are adversarial attacks in AI and their impact?

What are adversarial attacks in AI and how do they affect machine learning models?

Adversarial attacks are techniques used to fool AI models by introducing subtle perturbations to input data, leading to incorrect predictions.

Can you give me an example of an adversarial attack?

Sure! One common example is adding noise to an image, which can cause an image recognition model to misclassify it.

What are the potential consequences of these attacks?

The consequences can be severe, including compromised security in applications like facial recognition, autonomous vehicles, and more.

How can we defend against adversarial attacks?

Defenses include adversarial training, input preprocessing, and using robust models that are less sensitive to perturbations.