What are adversarial attacks?

What are adversarial attacks in machine learning?

Adversarial attacks are techniques used to fool machine learning models by introducing small, often imperceptible, perturbations to the input data.

Can you give me an example of an adversarial attack?

Sure! One common example is adding noise to an image, which can cause a model to misclassify it, like changing a cat image to be recognized as a dog.

How do these attacks affect AI systems?

They can significantly undermine the reliability and security of AI systems, especially in critical applications like autonomous driving or facial recognition.

What measures can be taken to defend against adversarial attacks?

Defensive strategies include adversarial training, where models are trained on both clean and adversarial examples, and using techniques like input preprocessing.