Use a real-life example of an AI system to discuss some impacts of cyber attacks : Building an Image Classification Task

The video tutorial discusses securing image classification systems against adversarial attacks. It covers the importance of understanding the architecture, algorithm, and dataset when analyzing AI security. The video highlights common algorithms like VGG, ResNet, and Inception, and discusses datasets such as MNIST, CIFAR, and ImageNet. The tutorial emphasizes testing different attacks and defenses on these models and datasets to gain accurate insights. The video concludes with a preview of the next topic on various attacks.

What are the three key aspects to consider when analyzing the security of an AI solution for image classification?

• User interface, design, and functionality

• Speed, accuracy, and cost

• Hardware, software, and network

• Architecture, algorithm, and dataset

Which model is known for addressing the vanishing gradient problem?

• VGG

• ResNet

• Inception

• AlexNet

Why is the VGG network not recommended for practical use despite being commonly used for testing attacks?

• It is too fast and small

• It lacks layers

• It is slow for training and large in size

• It is not compatible with GPUs

Which dataset is recommended for developing a production-based solution due to its comprehensive nature?

• MNIST

• Fashion-MNIST

• CIFAR

• ImageNet

What is a significant consideration when using MNIST or CIFAR datasets for testing?

• They allow faster attack simulations

• They are only for text classification

• They are not suitable for quick tests

• They require a lot of memory