CloudLeak: DNN Model Extractions from Commercial MLaaS Platforms
0Yier Jin | Associate Professor, University of Florida
Honggang Yu | PhD Student, University of Florida
Tsung-Yi Ho | Professor, National Tsing Hua University
Date: Wednesday, August 5 | 10:00am-10:40am
Format: 40-Minute Briefings
Tracks: AI, ML, & Data Science, Cloud & Platform Security
Deep Neural Networks (DNN) have been widely deployed for a variety of tasks across many disciplines, for example, image processing, natural language processing, and voice recognition. However, creating a successful DNN model depends on the availability of huge amounts of data as well as enormous computing power, and the model training is often an arduously slow process. This presents a large barrier to those interested in utilizing a DNN. To meet the demands of users who may not have sufficient resources, cloud-based deep learning services arose as a cost-effective and flexible solution allowing users to complete their machine learning (ML) tasks efficiently. Machine Learning as a Service (MLaaS) platform providers may spend great effort collecting data and training models, and thus want to keep them proprietary. The DNN models of MLaaS platforms can only be used as web-based API interface and thus is isolated from users. In this work, we develop a novel type of attack that allows the adversary to easily extract the large-scale DNN models from various cloud-based MLaaS platforms, which are hosted by Microsoft, Face++, IBM, Google and Clarifai.
Black Hat - USA - 2020 Hacking conference
#hacking, #hackers, #infosec, #opsec, #IT, #security