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 …

We present a new encoder for hiding parameters in an interval membership function. As an
application, we design a simple and efficient virtual black-box obfuscator for evasive …

Federated learning enables multiple participants to collaboratively train a model without
aggregating the training data. Although the training data are kept within each participant and …

Public intelligent services enabled by machine learning algorithms are vulnerable to model
extraction attacks that can steal confidential information of the learning models through …

Machine learning model is vulnerable to model extraction attacks since the attackers can
send plenty of queries to infer the hyperparameters of the machine learning model thus …

Model extraction attacks (MEAs) allow adversaries to replicate a surrogate model analogous
to the target model's decision pattern. While several attacks and defenses have been …

Model extraction attacks have become serious issues for service providers using machine
learning. We consider an adversarial setting to prevent model extraction under the …

Abstract Deep Neural Networks (DNNs) have been widely applied to diverse real life
applications and dominated in most cases. Considering the hardware consumption for DNN …

Machine learning models are typically made available to potential client users via inference
APIs. Model extraction attacks occur when a malicious client uses information gleaned from …

The privacy of machine learning models has become a significant concern in many
emerging Machine-Learning-as-a-Service applications, where prediction services based on …