Use a real-life example of an AI system to discuss some impacts of cyber attacks : Attacks Against ML with Examples

This video tutorial introduces adversarial attacks in machine learning, explaining how adversarial examples can lead to incorrect model predictions. It covers the concept of decision boundaries and how adversarial examples exploit these boundaries. The tutorial delves into the mechanics of adversarial attacks, focusing on the role of gradients and LP norms in perturbing inputs. It also explains loss functions and gradients, highlighting their importance in model accuracy. Finally, the video discusses optimal perturbation attacks and the measurement of input changes using LP norms.

What are adversarial examples and how do they affect machine learning models?

Explain the concept of decision boundaries in the context of adversarial attacks.

Describe how gradient information is used in adversarial attacks.

What is the significance of the loss function in machine learning models?

What are optimal perturbation attacks and how do they relate to decision boundaries?