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 is the primary goal of adversarial examples in machine learning?

• To improve model accuracy

• To cause models to make incorrect predictions

• To enhance data security

• To simplify decision boundaries

How do adversarial attacks typically determine the necessary changes to inputs?

• By using random noise

• By calculating the gradient of the loss function

• By analyzing the model's architecture

• By increasing the dataset size

What does the term 'gradient' refer to in the context of machine learning?

• A method for data normalization

• A direction of the greatest increase of a function

• A type of loss function

• A measure of data quality

Which LP norm measures the maximum change of all pixels in a sample?

• L0 norm

• L1 norm

• L Infinity norm

• L2 norm

What is the purpose of a loss function in machine learning?

• To define the model's architecture

• To measure how well a model makes predictions

• To increase the dataset size

• To simplify the training process