作者
Buse Gul Atli, Sebastian Szyller, Mika Juuti, Samuel Marchal, N Asokan
发表日期
2020
研讨会论文
Engineering Dependable and Secure Machine Learning Systems: Third International Workshop, EDSMLS 2020, New York City, NY, USA, February 7, 2020, Revised Selected Papers 3
页码范围
42-57
出版商
Springer International Publishing
简介
Recently, machine learning (ML) has introduced advanced solutions to many domains. Since ML models provide business advantage to model owners, protecting intellectual property of ML models has emerged as an important consideration. Confidentiality of ML models can be protected by exposing them to clients only via prediction APIs. However, model extraction attacks can steal the functionality of ML models using the information leaked to clients through the results returned via the API. In this work, we question whether model extraction is a serious threat to complex, real-life ML models. We evaluate the current state-of-the-art model extraction attack (Knockoff nets) against complex models. We reproduce and confirm the results in the original paper. But we also show that the performance of this attack can be limited by several factors, including ML model architecture and the granularity of API response …
学术搜索中的文章
BG Atli, S Szyller, M Juuti, S Marchal, N Asokan - … Dependable and Secure Machine Learning Systems …, 2020