Model extraction attacks are a kind of attacks where an adversary obtains a machine
learning model whose performance is comparable with one of the victim model through …

Counterfactuals provide guidance on achieving a favorable outcome from a model, with
minimum input perturbation. However, counterfactuals can also be exploited to leak …

Model extraction attacks are designed to steal trained models with only query access, as is
often provided through APIs that ML-as-a-Service providers offer. ML models are expensive …

We study design of black-box model extraction attacks that can send minimal number of
queries from a publicly available dataset to a target ML model through a predictive API with …

Cloud service providers, including Google, Amazon, and Alibaba, have now launched
machinelearning-as-a-service (MLaaS) platforms, allowing clients to access sophisticated …

Recent studies show that machine learning models are vulnerable to model extraction
attacks, where the adversary builds a substitute model that achieves almost the same …

The outstanding performance of deep learning has prompted the rise of Machine Learning
as a Service (MLaaS), which significantly reduces the difficulty for users to train and deploy …

Abstract Machine learning models are increasingly being offered as a service by big
companies such as Google, Microsoft and Amazon. They use Machine Learning as a …

Machine learning models are vulnerable to simple model stealing attacks if the adversary
can obtain output labels for chosen inputs. To protect against these attacks, it has been …

As a promising service, Machine Learning as a Service (MLaaS) provides personalized
inference functions for clients through paid APIs. Nevertheless, it is vulnerable to model …