The Geometry of Last-Layer Model Stealing
Snigdha Chandan Khilar
cs.LG
Abstract
This paper uses geometry to explain how a machine learning model can be stolen using an already existing well-known method. The author has shown the exact conditions required to perfectly copy the final layer of a transformer network. When looking deeper into the hidden layers the author has explained clear limits. The author has also demonstrated that a hidden network cannot be fully reverse engineered just by looking at the final results. The research clearly maps out what can and cannot be stolen from a model.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.