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 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 …

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 …

With the proliferation of machine learning models in diverse applications, the issue of model
security has increasingly become a focal point. Model steal attacks can cause significant …

Abstract Machine Learning (ML) models become vulnerable to Model Stealing Attacks
(MSA) when they are deployed as a service. In such attacks, the deployed model is queried …

High-performance Deep Neural Networks (DNNs) are increasingly deployed in many real-
world applications eg, cloud prediction APIs. Recent advances in model functionality …

Model stealing attacks have become a serious concern for deep learning models, where an
attacker can steal a trained model by querying its black-box API. This can lead to intellectual …

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

Cloud-based Machine Learning as a Service (MLaaS) is gradually gaining acceptance as a
reliable solution to various real-life scenarios. These services typically utilize Deep Neural …

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 …