  While deep learning models have shown significant performance across various
domains, their deployment needs extensive resources and advanced computing
infrastructure. As a solution, Machine Learning as a Service (MLaaS) has
emerged, lowering the barriers for users to release or productize their deep
learning models. However, previous studies have highlighted potential privacy
and security concerns associated with MLaaS, and one primary threat is model
extraction attacks. To address this, there are many defense solutions but they
suffer from unrealistic assumptions and generalization issues, making them less
practical for reliable protection. Driven by these limitations, we introduce a
novel defense mechanism, SAME, based on the concept of sample reconstruction.
This strategy imposes minimal prerequisites on the defender's capabilities,
eliminating the need for auxiliary Out-of-Distribution (OOD) datasets, user
query history, white-box model access, and additional intervention during model
training. It is compatible with existing active defense methods. Our extensive
experiments corroborate the superior efficacy of SAME over state-of-the-art
solutions. Our code is available at https://github.com/xythink/SAME.
