What are adversarial attacks in machine learning?

What are adversarial attacks in machine learning?

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

How do these attacks affect the performance of ML models?

They can significantly degrade model performance, leading to incorrect predictions and undermining the model's reliability.

Can you give an example of an adversarial attack?

Sure! One common example is the Fast Gradient Sign Method (FGSM), which alters input images to mislead image classifiers.

What measures can be taken to defend against these attacks?

Defensive strategies include adversarial training, input preprocessing, and using robust model architectures.

Are there any real-world implications of these attacks?

Yes, adversarial attacks can have serious implications in areas like autonomous driving, security systems, and healthcare.