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

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 …

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 …

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 (ML) applications are increasingly prevalent. Protecting the confidentiality
of ML models becomes paramount for two reasons:(a) a model can be a business …

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 …

One major goal of the AI security community is to securely and reliably produce and deploy
deep learning models for real-world applications. To this end, data poisoning based …

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where adversaries
embed a hidden backdoor trigger during the training process for malicious prediction …

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

Recent work proposed the concept of backdoor attacks on deep neural networks (DNNs),
where misclassification rules are hidden inside normal models, only to be triggered by very …