| Abstract: |
| The problem of model extraction in machine learning
has been studied for over thirty years.
Its most challenging goal --- functionally equivalent extraction
in the black-box setting --- is achieved via parameter recovery.
Since Crypto 2020, researchers have made significant progress
by approaching it through the lens of cryptanalysis.
In this talk, we will briefly review this problem and
introduce some recent results in this emerging direction,
with a special focus on attacks in the hard-label setting. |
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