Obtaining a well-trained model involves expensive data collection and training procedures,
therefore the model is a valuable intellectual property. Recent studies revealed that …

With increasingly more data and computation involved in their training, machine learning
models constitute valuable intellectual property. This has spurred interest in model stealing …

In model extraction attacks, adversaries can steal a machine learning model exposed via a
public API by repeatedly querying it and adjusting their own model based on obtained …

Current model extraction attacks assume that the adversary has access to a surrogate
dataset with characteristics similar to the proprietary data used to train the victim model. This …

Abstract Machine learning models deployed as a service (MLaaS) are susceptible to model
stealing attacks, where an adversary attempts to steal the model within a restricted access …

Self-supervised models are increasingly prevalent in machine learning (ML) since they
reduce the need for expensively labeled data. Because of their versatility in downstream …

Abstract Deep Neural Networks (DNNs) are susceptible to model stealing attacks, which
allows a data-limited adversary with no knowledge of the training dataset to clone the …

Model extraction attacks aim to duplicate a machine learning model through query access to
a target model. Early studies mainly focus on discriminative models. Despite the success …

Training a Deep Learning (DL) model requires proprietary data and computing-intensive
resources. To recoup their training costs, a model provider can monetize DL models through …

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 …