What are adversarial attacks in AI?

What are adversarial attacks in AI?

Adversarial attacks are techniques used to fool AI models by providing deceptive inputs that lead to incorrect outputs.

Can you give me an example of an adversarial attack?

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

How do these attacks affect machine learning models?

They can significantly undermine the reliability and security of machine learning models, making them vulnerable to exploitation.

What can be done to defend against adversarial attacks?

Defenses include adversarial training, input preprocessing, and using robust models designed to withstand such attacks.