Evaluate the impact of privacy issues, cyberattacks, and malware on your AI application : Testing Practical Defense from

The video tutorial discusses defense-enhanced models, focusing on adversarial training to improve model robustness. It covers Natural Adversarial Training (NAT) and Ensemble Adversarial Defense (ET), highlighting their implementation and results. The tutorial also explains how to evaluate these defenses using specific metrics, ensuring the new models maintain accuracy while being more resistant to attacks.

What is the primary goal of retraining models with adversarial examples?

• To reduce the model's size

• To improve the model's robustness against attacks

• To increase the model's speed

• To enhance the model's user interface

Which method is known as NAT in adversarial training?

• Node Adjustment Technique

• Neural Adaptive Training

• Network Adversarial Tuning

• Natural Adversarial Training

What is the main advantage of the Ensemble Adversarial Defense method?

• It generates adversarial examples with various attacks

• It uses a single attack type

• It focuses on reducing computational cost

• It simplifies the model architecture

How does the Ensemble Adversarial Defense improve model performance?

• By reducing training time

• By increasing the number of layers

• By achieving higher accuracy and better protection

• By simplifying the data input process

What is the purpose of running an evaluation script on defense methods?

• To compare defense methods using various metrics

• To enhance the model's user interface

• To increase the model's speed

• To reduce the model's size