Model Decision Boundaries
Florian explains the process of finding decision points in high-dimensional spaces for machine learning models. Kyle discusses the complexity of different functions and the number of queries needed for model approximation. Florian compares the efficiency of attacks based on access to confidence scores versus only class labels, highlighting the differences in query requirements.In this clip
From this podcast
Data Skeptic
Stealing Models from the Cloud
Related Questions