Use a real-life example of an AI system to discuss some impacts of cyber attacks : Adversarial Attacks and Their Distinc

The video tutorial discusses adversarial attacks on AI systems, focusing on how these attacks vary based on the environment and the attacker's goals. It outlines six properties of adversarial attacks, including the type of attack, the environment, and the knowledge level of the attacker. The tutorial also provides recommendations for testing AI solutions by understanding the constraints and selecting appropriate attacks based on these properties.

What is the primary goal of an attacker in a face detection system?

• To gain unauthorized access

• To steal data

• To improve system accuracy

• To cause misclassification

How does the environment affect adversarial attacks?

• It has no effect

• It only affects digital attacks

• It can make attacks ineffective

• It enhances attack success

What is a characteristic of iterative attacks?

• They are faster but less accurate

• They require multiple interactions

• They are only used in digital environments

• They are always successful

What does a white box attack imply?

• Limited knowledge of the system

• Complete knowledge of the system

• No knowledge of the system

• Knowledge of only the data set

Which norm is most frequently used in adversarial examples?

• L Infinity norm

• L2 norm

• L1 norm

• L0 norm