What is adversarial attack detection in AI?

What is adversarial attack detection in AI?

Adversarial attack detection refers to methods used to identify and mitigate attacks on AI models, where inputs are intentionally manipulated to deceive the model.

Why are adversarial attacks a concern for AI systems?

They can significantly undermine the reliability and safety of AI systems, leading to incorrect predictions or decisions.

How can we defend against these attacks?

Defenses include adversarial training, input preprocessing, and using robust model architectures to enhance resilience.

Can you give an example of an adversarial attack?

Sure! A common example is adding small noise to an image that causes a model to misclassify it, like changing a cat image to be recognized as a dog.