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.

Explain the differences between the three most common image classification datasets mentioned.

What challenges might arise when using ImageNet for developing a production-based solution?