Abstract Machine learning models, especially deep neural network (DNN) models, have
widespread and valuable applications in business activities. Training a deep learning model …

Model extraction attack typically refers to extracting non-public information from a black-box
machine learning model. Its unauthorized nature poses significant threat to intellectual …

Recently, machine learning (ML) has introduced advanced solutions to many domains.
Since ML models provide business advantage to model owners, protecting intellectual …

Deep neural networks (DNNs) have become the essential components for various
commercialized machine learning services, such as Machine Learning as a Service …

Several recent works have demonstrated highly effective model stealing (MS) attacks on
Deep Neural Networks (DNNs) in black-box settings, even when the training data is …

It usually takes a lot of time and resources to train a high-accurate Machine Learning model,
so it is believed that the trainer owns the Intellectual Property (IP) of the model. With the help …

Recent studies have demonstrated that Deep Neural Networks (DNNs) are vulnerable to
model extraction attacks. In these attacks, the malicious users utilize Out-Of-Distribution …

Machine learning architectures are readily available, but obtaining the high quality labeled
data for training is costly. Pre-trained models available as cloud services can be used to …

As a new paradigm to erase data from a model and protect user privacy, machine
unlearning has drawn significant attention. However, existing studies on machine …

Physical Unclonable Function (PUF) is a promising hardware security primitive with broad
application prospect. However, the strong PUF with numerous Challenge and Response …