Use a real-life example of an AI system to discuss some impacts of cyber attacks : Attacks on Classification and How The

The video tutorial discusses the vulnerability of machine learning tasks, focusing on classification. It introduces the first attack method, 'lobes', and its limitations. The fast gradient sign method (fsem) is then explained as a more efficient approach for adversarial attacks. The tutorial details how to create targeted adversarial attacks by modifying images to deceive neural networks. Finally, it concludes with a transition to practical application, demonstrating the attack on a classification task.

What was the main drawback of the 'lobes' attack method?

• It was too slow for large images.

• It was not precise.

• It was too complex to implement.

• It was not universal.

What is the primary goal of the Fast Gradient Sign Method (FGSM)?

• To increase the size of the image.

• To make images visually different from the original class.

• To minimize the loss function.

• To make images appear as a different class while looking similar.

How does FGSM determine the direction for perturbations?

• By analyzing the color of the image.

• By using the gradient of the loss function.

• By checking the size of the image.

• By comparing with a reference image.

What is the purpose of a targeted adversarial attack?

• To make the image unrecognizable.

• To increase the probability of predicting a specific target label.

• To reduce the size of the image.

• To make the image identical to another class.

Which class label is used in a targeted attack to compute adversarial perturbations?

• The original class label.

• The least likely class label.

• The most likely class label.

• A random class label.