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 this https URL

通过基于样本重构的概念，介绍了一种新颖的防御机制 SAME，能够解决深度学习模型在 MLaaS 环境下的模型提取攻击问题，并且相较于现有解决方案具有更强的防御效果。

SAME：对模型提取攻击的样本重构

SAME: Sample Reconstruction Against Model Extraction Attacks

Over the last years, word and sentence embeddings have established as text
preprocessing for all kinds of NLP tasks and improved performances in these
tasks significantly. Unfortunately, it has also been shown that these
embeddings inherit various kinds of biases from the training data and thereby
pass on biases present in society to NLP solutions. Many papers attempted to
quantify bias in word or sentence embeddings to evaluate debiasing methods or
compare different embedding models, often with cosine-based scores. However,
some works have raised doubts about these scores showing that even though they
report low biases, biases persist and can be shown with other tests. In fact,
there is a great variety of bias scores or tests proposed in the literature
without any consensus on the optimal solutions. We lack works that study the
behavior of bias scores and elaborate their advantages and disadvantages. In
this work, we will explore different cosine-based bias scores. We provide a
bias definition based on the ideas from the literature and derive novel
requirements for bias scores. Furthermore, we thoroughly investigate the
existing cosine-based scores and their limitations in order to show why these
scores fail to report biases in some situations. Finally, we propose a new bias
score, SAME, to address the shortcomings of existing bias scores and show
empirically that SAME is better suited to quantify biases in word embeddings.

本文研究了利用余弦相似度对词嵌入进行编码的偏见评分，提出了一种新的偏见评分方法 SAME，并用实验证明其比现有的评分方法更能准确评估词嵌入中的偏见。