Explain the negative impacts of artificial intelligence systems on society : Adversarial Attacks Metrics and White-Box A

The video tutorial covers key metrics for evaluating adversarial attacks, focusing on misclassification, imperceptibility, robustness, and speed. It explains how these metrics are used to compare different attacks, highlighting the importance of misclassification rate, average confidence, and imperceptibility measures like LP distortion and structural similarity. Robustness is discussed in terms of noise tolerance and image-specific measures, while speed is noted for its relevance in recent attacks. The tutorial concludes with a summary and a preview of future content on practical execution of attacks.

Describe the importance of perceptibility in the context of adversarial attacks.

How does noise tolerance estimation contribute to the robustness of adversarial examples?