Use a real-life example of an AI system to discuss some impacts of cyber attacks : Practical Poisoning Attacks

The video explains poisoning attacks in AI, where attackers inject malicious data into training sets. It uses the Adversarial Robustness Toolbox (ART) by IBM to demonstrate a poisoning attack on a digit recognition system. The demo shows how poisoned data can alter AI predictions, such as recognizing a zero as a one. To counteract this, the video introduces defense methods like clustering and PCA to detect poisoned images. The video concludes with a brief mention of upcoming content on privacy attacks.

What is a poisoning attack in the context of AI models?

• An attack that physically damages the hardware of AI systems.

• An attack that involves injecting malicious data into the training set.

• An attack that steals data from AI models.

• An attack that slows down the processing speed of AI systems.

Which tool is used in the video to demonstrate a poisoning attack?

• Adversarial Robustness Toolbox (ART)

• PyTorch

• Scikit-learn

• TensorFlow

In the poisoning demo, what is the main goal of the attack on the digit recognition system?

• To improve the accuracy of the system.

• To make the system faster.

• To cause the system to misclassify digits.

• To enhance the system's security.

What method is suggested to detect poisoned images in the video?

• Increasing the size of the training dataset.

• Using a different AI model.

• Applying clustering and PCA methods.

• Using a firewall to block malicious data.

How are poisoned images visually represented in the defense mechanism discussed?

• As images with a different color scheme.

• As a separate cluster distinct from normal images.

• As random noise in the dataset.

• As a single cluster with normal images.