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 …

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

Abstract Data-Free Model Extraction (DFME) aims to clone a black-box model without
knowing its original training data distribution, making it much easier for attackers to steal …

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 …

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 …

The widespread use of deep learning technology across various industries has made deep
neural network models highly valuable and, as a result, attractive targets for potential …

Despite the broad application of Machine Learning models as a Service (MLaaS), they are
vulnerable to model stealing attacks. These attacks can replicate the model functionality by …

While deep learning models have shown significant performance across various domains,
their deployment needs extensive resources and advanced computing infrastructure. As a …

Explainable AI encourages machine learning applications in the real world, whereas data-
free model extraction attacks (DFME), in which an adversary steals a trained machine …

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 …